Power Allocation Optimization Under Constraints of Throughput Requirements and Interference Limit for Cognitive Radio Networks

ABSTRACT

The present invention provides a method of power control for a plurality of secondary users (SUs), or unlicensed users, in a cognitive radio network having a plurality of primary users (PUs), or licensed users. The method comprises determining an optimal power level for each of the SUs such that a total throughput of all the SUs is maximized while satisfying an individual throughput requirement of each SU and an interference limit constraint of the PUs. In particular, a case having two SUs is considered.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Patent Application No. 62/197,016, filed on Jul. 26, 2015, which is incorporated by reference herein in its entirety.

BACKGROUND Field of the Invention

The present invention relates to power control for a cognitive radio communication system.

LIST OF REFERENCES

There follows a list of references that are occasionally cited in the specification. Each of the disclosures of these references is incorporated by reference herein in its entirety.

-   [1] Y. C. Liang, K. C. Chen, G. Y. Li, and P. Mahonen: Cognitive     Radio Networking and Communications: An Overview. IEEE Transactions     on Vehicular Technology, vol. 60, no. 7, pp. 3386-3407, (2011) -   [2] Y. Liu, L. X. Cai, H. Luo, and X. Shen: Deploying Cognitive     Cellular Networks under Dynamic Resource Management. IEEE Wireless     Communications, Vol. 20, No. 2, pp. 82-88, (2013) -   [3] A. Gjendemsio, D. Gesbert, G. E. Oien, and S. G. Kiani: Optimal     Power Allocation and Scheduling for Two-Cell Capacity Maximization.     Proceedings of International Symposium on Modeling and Optimization     in Mobile, Ad Hoc and Wireless Networks (2006) -   [4] C. S. Chen, G. E. Oien: Optimal Power Allocation for Two-Cell     Sum Rate Maximization under Minimum Rate Constraints. Proceedings of     IEEE International Symposium on Wireless Communication Systems     (2008) -   [5] V. Chandrasekhar, Z. K. Shen: Optimal Uplink Power Control in     Two-Cell Systems with Rise-Over-Thermal Constraints. IEEE     Communications Letters, vol. 12, no. 3, pp. 173-175, (2008) -   [6] C. H. Yu, K. Doppler, C. B. Ribeiro, and O. Tirkkonen: Resource     Sharing Optimization for Device-to-Device Communication Underlaying     Cellular Networks. IEEE Transactions on Wireless Communications,     vol. 10, no. 8, pp. 2752-2763, (2011) -   [7] Y. Wu, T. Y. Zhang, Danny H. K. Tsang: Joint Pricing and Power     Allocation for Dynamic Spectrum Access Networks with Stackelberg     Game Model. IEEE Transactions on Wireless Communications, vol. 10,     no. 1, pp. 12-19, (2011) -   [8] Y. Wu, Q. H. Zhu, J. W Huang, Danny H. K. Tsang: Revenue Sharing     based Resource Allocation for Dynamic Spectrum Access Networks. IEEE     Journal on Selected Areas in Communications, vol. 32, no. 11, pp.     2280-2297, (2014) -   [9] J. W. Huang, R. Berry, and M. L. Honig: Auction-Based Spectrum     Sharing. ACM MONET, vol. 11, no. 3, pp. 405-418, (2006)

DESCRIPTION OF RELATED ART

With a rapid growth of wireless services in the past decades, the issue of spectrum congestion has attracted a lot of research interest. Spectrum sharing via cognitive radio (CR), in which unlicensed users are eligible to share the spectrum with the licensed users, has been considered as a promising approach to improve the spectrum utilization and to relieve the spectrum congestion problem [1], [2], In the art, a licensed user and an unlicensed user are also called as a primary user (PU) and a secondary user (SU), respectively. The premise of the spectrum sharing via CR lies in that the transmissions of SUs cannot cause a harmful influence to the PU, which consequently necessitates a careful design of power control such that (i) each SU can obtain a satisfactory throughput, and (ii) the aggregate interference caused by the SUs' transmissions does not exceed a tolerable threshold set by the PU.

To the best of the inventors' knowledge, it is an open topic in the art to analytically derive the SUs' optimal power allocations when the PU's interference limit, a critical property for spectrum sharing cognitive radio, is taken into account. Although there exist previous works investigating the power allocations for multiple SUs subject to the PUs' interference limit constraints by using algorithmic approaches [7]-[9], these works seldom can derive the optimal power allocations analytically.

There is a need in the art for analytically deriving optimal power allocations for SUs under PUs' interference limit constraints so as to apply the derived power allocations for cognitive radio networks.

SUMMARY OF THE INVENTION

An aspect of the present invention is to provide a method of power control for a plurality of SUs in a cognitive radio network having a plurality of PUs. The method comprises determining an optimal power level for each of the SUs such that a total throughput of all the SUs is maximized while satisfying an individual throughput requirement of each SU and an interference limit constraint of the PUs.

Other aspects of the present invention are disclosed as illustrated by the embodiments hereinafter.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts different cases of the feasible region of Problem (P1) according to the different locations of Q relative to l₁ and l₂.

FIG. 2 depicts different subcases in Case I according to the different locations of line l₀ relative to points X₁ and Y₂.

FIG. 3 depicts Subcase II. 2 and Subcase II.3 in Case II according to the different locations of line l₀ relative to points Y₁ and Y₂, whereas Subcase II.1 is given in FIG. 1(b).

FIG. 4 depicts sum throughput F(p₁,p₂) with different interference limits (i.e., Γ) of the PU for the case of A≠0, where the channel power gains are g₁₁=0.2, g₂₂=0.4, g₁₂=g₂₁=0.01, g_(1B)=0.15 and g_(2B)=0.1, and the SUs' minimum SINR requirements are γ₁=γ₂=2.

FIG. 5 depicts sum throughput F(p₁,p₂) with different levels of cross interference (i.e., g₁₂ and g₂₁) for the case of A≠0, where the rest of the channel gains are g₁₁=0.2, g₂₂=0.4, g_(1B)=0.15 and g_(2B)=0.1, and the SUs' minimum SINR requirements are γ₁=γ₂=2. The PU's interference limit is Γ=0.1.

FIG. 6 depicts sum throughput F(p₁,p₂) with different interference limits (i.e. Γ) of the PU for the case of A=0, where the channel power gains are g₁₁=g₂₂=0.2, g₁₂=g₂₁=0.02 and g_(1B)=g_(2B)=0.1. the SUs' minimum SINR requirements are γ₁=γ₂=2.

DETAILED DESCRIPTION

The present work aims at analytically deriving optimal power allocations for maximizing the SUs' total throughput while taking into account each SU's individual throughput requirement and the PU's interference limit constraint.

To analytically derive the optimal power allocations, we focus on the case of two SUs sharing the spectrum of a PU. In particular, the problem is similar to the two-users transmit power control problem in wireless interference channel, which has been studied in [3]-[6] but without taking into account the PU's interference limit. Specifically, [3] investigated the power allocations for two mobile users in two different cells but only considered each user's peak transmit power constraint. As an extension of [3], [4] further considered each user's minimum throughput requirement. Both [3] and [4] showed that the optimal power allocations exhibited a so-called binary property. Reference [5] formulated a rise-over-thermal constraint (which can be treated as the interference limit at each mobile user) and revealed that the optimal power allocations only resided on one of the vertexes of the feasible region. Reference [6] investigated the power allocations for two interfering device-to-device users with both having the maximum and minimum rate constraints. Again, the optimal power allocations were found only at the vertexes of the feasible region.

Different from the previous works [7]-[9], we find that besides the vertexes of the feasible region, the SUs' optimal power allocations might also reside on the boundary of the feasible region that corresponds to the PU's interference limit (which thus are more general than the previous works). We validate this result via extensive numerical simulations and also show how the PU's interference limit influences the derived optimal power allocations.

A. System Model, Problem Formulation, and Solvability of the Problem A.1 System Model and Problem Formulation

We consider a scenario of two SUs sharing the licensed spectrum of one PU. Each SU comprises a transmitter and a receiver, and the PU performs the uplink transmission to the base station (BS). Let p₁ and p₂ denote transmit powers of SU1 and SU2, respectively. We consider the two-user throughput maximization problem as follows:

$\begin{matrix} {{{\left( {P\; 1} \right)\text{:}\mspace{14mu} \max \; {F\left( {p_{1},p_{2}} \right)}} = {{W\; {\log_{2}\left( {1 + \frac{p_{1}g_{11}}{n_{1} + {p_{2}g_{21}}}} \right)}} + {W\; {\log_{2}\left( {1 + \frac{p_{2}g_{22}}{n_{2} + {p_{1}g_{12}}}} \right)}}}}\mspace{20mu} {{{{{subject}\mspace{14mu} {to}\text{:}\mspace{14mu} p_{1}g_{1B}} + {p_{2}g_{2B}}} \leq \Gamma},}} & (1) \\ {\mspace{20mu} {{\frac{p_{1}g_{11}}{n_{1} + {p_{2}g_{21}}} \geq \gamma_{1}},}} & (2) \\ {\mspace{20mu} {{\frac{p_{2}g_{22}}{n_{2} + {p_{1}g_{12}}} \geq \gamma_{2}},}} & (3) \\ {\mspace{20mu} {{0 \leq p_{1} \leq p_{1}^{{ma}\; x}},{{{and}\mspace{14mu} 0} \leq p_{2} \leq {p_{2}^{{ma}\; x}.}}}} & (4) \end{matrix}$

Specifically, g_(ii) denotes the channel power gain from the transmitter of SU i to its receiver, g_(ij) denotes the channel power gain from the transmitter of SU i to the receiver of SU j, and g_(iB) denotes the channel power gain from the transmitter of SU i to the BS. Besides, n_(i) denotes the power of the background noise experienced by the receiver of SU i (Notice that n_(i) can also incorporate the interference from the PU to SU i), and W denotes the bandwidth of the PU's channel. Without loss of generality, W is assumed to be unity (i.e. W=1) hereinafter for easy presentation. Constraint (1) ensures that the total interference from the two SUs cannot exceed the PU's interference limit, which is denoted by T. Constraints (2) and (3) ensure that each SU achieves its required signal to interference plus noise ratio denoted by γ_(i) (or equivalently, each SU i achieves its required throughput log₂(1+γ_(i)) measured by the channel capacity formula). Finally, constraint (4) ensures that each SU i's transmit power cannot exceed the upper bound p_(i) ^(max). Hereinafter, we use (p*₁,p*₂) to denote the optimal power allocations of the two SUs of Problem (P1).

A.2 Feasible Region and Feasibility of Problem (P1)

It is apparent that (p*₁,p*₂) strongly depends on the feasible region comprised of (1)-(4). To geographically characterize the feasible region of (P1), we first define the following five lines:

$\begin{matrix} {{{{Line}\mspace{14mu} l_{0}\text{:}\mspace{14mu} p_{2}} = {{f_{l_{0}}\left( p_{1} \right)} = {{{- \frac{g_{1B}}{g_{2B}}}p_{1}} + \frac{\Gamma}{g_{2B}}}}},} & (5) \\ {{{{Line}\mspace{14mu} l_{1}\text{:}\mspace{14mu} p_{2}} = {{f_{l_{1}}\left( p_{1} \right)} = {{\frac{g_{11}}{\gamma_{1}g_{21}}p_{1}} - \frac{n_{1}}{g_{21}}}}},} & (6) \\ {{{{Line}\mspace{14mu} l_{2}\text{:}\mspace{14mu} p_{2}} = {{f_{l_{2}}\left( p_{1} \right)} = {{\frac{\gamma_{2}g_{12}}{g_{22}}p_{1}} + \frac{\gamma_{2}n_{2}}{g_{22}}}}},} & (7) \\ {{{{Line}\mspace{14mu} l_{m\; 1}\text{:}\mspace{14mu} p_{1}} = p_{1}^{{m\; {ax}}\;}},{{{and}\mspace{14mu} {Line}\mspace{14mu} l_{m\; 2}\text{:}\mspace{14mu} p_{2}} = p_{2}^{m\; {ax}}},} & (8) \end{matrix}$

where lines l₀, l₁, and l₂ are obtained by making constraints (1), (2), and (3) strictly binding, and lines l_(m1) and l_(m2) are obtained by fixing p₁=p₁ ^(max) and p₂=p₂ ^(max), respectively.

The above five lines together constrain the feasible region of Problem (P1) as shown in FIG. 1. Using the five lines, we further provide a set of critical points to be used in the rest of this work in Table 1. Specifically, Table 1 shows the coordinates of each critical point (in the first column) and the two lines whose intersection point corresponds to this critical point (in the second column). For instance, point Z₀ in Table 1 represents the pair of minimum power allocations ({circumflex over (p)}₁,{circumflex over (p)}₂)=((Z₀)_(h),(Z₀)_(v)) such that the SUs meet constraints (2) and (3) together exactly, where we use (Z₀)_(h) (or (Z₀)_(v)) to denote the horizontal-coordinate (or the vertical-coordinate) of point Z₀, with the subscript h representing “horizontal” and subscript v representing “vertical”. The same notations will be used in the rest of this work.

TABLE 1 List of Critical Points Critical Points and their Coordinates Lines Q = (p₁ ^(max), p₂ ^(max)) l_(m1), l_(m2) $X_{0} = \left( {\frac{\Gamma - {g_{2B}p_{2}^{\max}}}{g_{1B}},p_{2}^{\max}} \right)$ l₀, l_(m2) $X_{1} = \left( {\frac{{\gamma_{1}n_{1}} + {g_{21}\gamma_{1}p_{2}^{\max}}}{g_{11}},p_{2}^{\max}} \right)$ l₁, l_(m2) $X_{2} = \left( {\frac{{g_{22}p_{2}^{\max}} - {\gamma_{2}n_{2}}}{\gamma_{2}g_{12}},p_{2}^{\max}} \right)$ l₂, l_(m2) $Y_{0} = \left( {p_{1}^{\max},\frac{\Gamma - {g_{1B}p_{1}^{\max}}}{g_{2B}}} \right)$ l₀, l_(m1) $Y_{1} = \left( {p_{1}^{\max},\frac{{g_{11}p_{1}^{\max}} - {\gamma_{1}n_{1}}}{\gamma_{1}g_{21}}} \right)$ l₁, l_(m1) $Y_{2} = \left( {p_{1}^{\max},\frac{{n_{2}\gamma_{2}} + {g_{12}\gamma_{2}p_{1}^{\max}}}{g_{22}}} \right)$ l₂, l_(m1) $Z_{0} = \left( {\frac{{\gamma_{1}\gamma_{2}g_{21}n_{2}} + {\gamma_{1}n_{1}g_{22}}}{{g_{11}g_{22}} - {\gamma_{1}\gamma_{2}g_{12}g_{21}}},\frac{{n_{2}\gamma_{2}g_{11}} + {\gamma_{1}\gamma_{2}g_{12}n_{1}}}{{g_{11}g_{22}} - {\gamma_{1}\gamma_{2}g_{12}g_{21}}}} \right)$ l₁, l₂ $Z_{1} = \left( {\frac{{g_{21}\gamma_{1}\Gamma} + {g_{2B}n_{1}\gamma_{1}}}{{g_{11}g_{2B}} + {g_{1B}g_{21}\gamma_{1}}},\frac{{g_{11}\Gamma} - {g_{1B}n_{1}\gamma_{1}}}{{g_{11}g_{2B}} + {g_{1B}g_{21}\gamma_{1}}}} \right)$ l₀, l₁ $Z_{2} = \left( {\frac{{g_{22}\Gamma} - {g_{2B}n_{2}\gamma_{2}}}{{g_{22}g_{1B}} + {g_{2B}g_{12}\gamma_{2}}},\frac{{g_{12}\gamma_{2}\Gamma} + {g_{1B}n_{2}\gamma_{2}}}{{g_{22}g_{1B}} + {g_{2B}g_{12}\gamma_{2}}}} \right)$ l₀, l₂

As used herein, “Z₀≦Q” is used to represent element-wise no greater than, i.e. (Z₀)≦(Q)_(h) and (Z₀)_(v)≦(Q)_(v).

Using point Z₀, we can characterize the feasibility of Problem (P1) as follows.

Proposition 1. Problem (P1) is feasible if both conditions (C1) and (C2) below are met:

0≦Z ₀ ≦Q,  (C1): and

(Z ₀)_(v) ≦f _(l) ₀ ((Z ₀)_(h)).  (C2):

Proof:

Condition (C1) means that Z₀ is feasible to the transmit power capacities of the SUs in constraint (4), and condition (C2) means that Z₀ is feasible to the PU's interference limit in constraint (1). Recall that function (.) has been defined in (5).

Remark 1.

Condition (C1) in Proposition 1 is equivalent to

g ₁₁ g ₂₂−γ₁γ₂ g ₁₁ g ₂₁>0.  (9)

That is, the interference channel power gains should be weaker enough to accommodate the throughput requirements of both SUs. In particular, constraint (9) means that the slope of line l₁ in (6) should be greater than that of l₂ in (7) such that their intersection (i.e. point Z₀) exists.

B. Optimal Power Allocations of Problem (P1)

In this section, we focus on solving Problem (P1) analytically, under the condition that (P1) is feasible (that is, conditions (C1) and (C2) are met). In particular, to account for the most general case of the feasible region, we assume that f_(l) ₀ ((Q)_(h))≦(Q)_(v) holds. Otherwise, the PU's interference limit will not influence the feasible region of Problem (P1) at all. In this case, the optimal power allocations have been given in [4], Graphically, it means that point Q is above line l₀ (that is, points X₀ and Y₀ always exist as shown in FIGS. 1 and 2), and the interference limit Γ in constraint (1) indeed influences the feasible region.

A deep look at Problem (P1) shows that its feasible region can take the following three different cases (depending on the different positions of point Q relative to line l₁ and line l₂).

Case I: f_(l) ₁ ((Q)_(h))≧(Q)_(v) and f_(l) ₂ ((Q)_(h))≦(Q)_(v) hold, meaning that point Q is between lines l₁ and l₂, and points X₁ and Y₂ appear, as shown in FIG. 1(a). Note that functions f_(l) ₁ (.) and f_(l) ₂ (.) have been defined in (6) and (7), respectively.

Case II: f_(l) ₁ ((Q)_(h))≦(Q)_(v) and f_(l) ₂ ((Q)_(h))≦(Q)_(v) hold, meaning that point Q is above lines l₁ and l₂, and points Y₁ and Y₂ appear, as shown in FIG. 1(b).

Case III: f_(l) ₁ ((Q)_(h))≧(Q)_(v) and f_(l) ₂ ((Q)_(h))≧(Q)_(v) hold, meaning that point Q is below lines l₁ and l₂, and points X₁ and X₂ appear, as shown in FIG. 1(c).

We next provide the optimal power allocations of Problem (P1) for each of the above three cases. However, even we suppose that Case I happens, it is still difficult to derive the optimal power allocations analytically, since it strongly depends on the location of l₀ representing the PU's interference limit. To categorize the impact of l₀, we further consider the following four different subcases of Case I, depending on how line l₀ locates with respect to points X₁ and Y₂.

Subcase I.1: f_(l) ₀ ((X₁)_(h))≧(X₁)_(v) and f_(l) ₀ ((Y₂)_(h))≧(Y₂)_(v), meaning that both points X₁ and Y₂ are below line l₀, and points X₀ and Y₀ appear as shown in FIG. 2(a).

Subcase I.2: f_(l) ₀ ((X₁)_(h))≦(X₁)_(v) and f_(l) ₀ ((Y₂)_(h))≦(Y₂)_(v), meaning that both points X₁ and Y₂ are above line l₀, and points and Z₂ appear as shown in FIG. 2(b).

Subcase I.3: f_(l) ₀ ((X₁)_(h))≧(X₁)_(v) and f_(l) ₀ ((Y₂)_(h))≦(Y₂)_(v), meaning that point X₁ is below line l₀, and point Y₂ is above line l₀, and points X₀ and Z₂ appear as shown in FIG. 2(c).

Subcase I.4: f_(l) ₀ ((X₁)_(h))≦(X₁)_(v) and f_(l) ₀ ((Y₂)_(h))≧(Y₂)_(v), meaning that point X₁ is above line l₀, and point Y₂ is below line l₀, and points and Y₀ appear as shown in FIG. 2(d).

B.1 Optimal Power Allocations for Subcase I.1

In this subsection, we focus on analytically deriving the optimal power allocations for Subcase I.1. We first present the following result.

Lemma 1. The optimal power allocations (p*₁,p*₂) of Problem (P1) for Subcase I.1 (shown in FIG. 2(a)) reside within the vertexes X₀, X₁, Y₀, and Y₂, or on the line segment of l₀ between points X₀ and Y₀.

Proof.

Please refer to Appendix I.

To further characterize (p*₁,p*₂) that might appear on the line segment of l₀ between points X₀ and Y₀, we use (1) to substitute p₂ by p₁ and equivalently transform the objective function F(p₁,p₂) as:

$\begin{matrix} {{\hat{F}\left( p_{1} \right)} = {{\log_{2}\left( \frac{{g_{21}\Gamma} + {g_{2B}n_{1}} - {g_{21}g_{1B}p_{1}} + {g_{11}g_{2B}p_{1}}}{{g_{21}\Gamma} + {g_{2B}n_{1}} - {g_{21}g_{1B}p_{1}}} \right)} + {{\log_{2}\left( \frac{{g_{2B}n_{2}} + {g_{12}g_{2B}p_{1}} + {g_{22}\Gamma} - {g_{22}g_{1B}p_{1}}}{{g_{2B}n_{2}} + {g_{12}g_{2B}p_{1}}} \right)}.}}} & (10) \end{matrix}$

Moreover, the following result holds for the above {circumflex over (F)}(p₁).

Lemma 2. Function {circumflex over (F)}(p₁) has three different extremal points, which can be expressed as follows:

${p_{1}^{o_{1}} = {{\frac{{- B} + \sqrt{B^{2} - {4A\; C}}}{2A}\mspace{14mu} {if}\mspace{14mu} A} \neq 0}},{p_{1}^{o_{2}} = {{\frac{{- B} - \sqrt{B^{2} - {4A\; C}}}{2A}\mspace{14mu} {if}\mspace{14mu} A} \neq {0\mspace{14mu} {and}}}}$ ${p_{1}^{o_{3}} = {{\frac{- C}{B}\mspace{14mu} {if}\mspace{14mu} A} = 0}},$

where parameters A, B, and C are given by (11), (12) and (13), respectively, as shown below, where the superscripts o₁, o₂ and o₃ represent the three different cases of the extremal points:

$\begin{matrix} {A = {\frac{g_{2B}}{\ln \; 2}\left\lbrack {{g_{11}g_{12}{g_{2B}\left( {{n_{1}g_{1B}} + {g_{21}\Gamma}} \right)}\left( {{g_{12}g_{2B}} - {g_{22}g_{1B}}} \right)} - {g_{22}g_{21}{g_{1B}\left( {{g_{1B}n_{2}} + {g_{12}\Gamma}} \right)}\left( {{g_{21}g_{1B}} - {g_{11}g_{2B}}} \right)}} \right\rbrack}} & (11) \\ {B = {\frac{g_{2B}\left( {{n_{1}g_{2B}} + {g_{21}\Gamma}} \right)}{\ln \; 2} \times \left\lbrack {{g_{11}{g_{2B}\left( {{2g_{12}g_{2B}n_{2}} + {g_{12}g_{22}\Gamma} - {g_{22}g_{1B}n_{2}}} \right)}} - {{g_{22}\left( {{g_{1B}n_{2}} + {g_{12}\Gamma}} \right)}\left( {{g_{11}g_{12}} - {2g_{21}g_{1B}}} \right)}} \right\rbrack}} & (12) \\ {C = {\frac{g_{2B}\left( {{n_{1}g_{2B}} + {g_{21}\Gamma}} \right)}{\ln \; 2} \times {\quad\left\lbrack {{g_{11}g_{2B}{n_{2}\left( {{n_{2}g_{2B}} + {g_{22}\Gamma}} \right)}} - {{g_{22}\left( {{g_{1B}n_{2}} + {g_{12}\Gamma}} \right)}\left( {{n_{1}g_{2B}} + {g_{21}\Gamma}} \right)}} \right\rbrack}}} & (13) \end{matrix}$

Proof:

Please refer to Appendix II.

Lemma 2 and the equivalence between {circumflex over (F)}(p₁) and F(p₁,p₂) imply that the derived extremal points p₁ ^(o) ¹ , p₁ ^(o) ² , and p₁ ^(o) ³ could also possibly maximize F(p₁,p₂), depending on whether they fall within the line segment of l₀ between points X₀ and Y₀ (i.e.

$\left\lbrack {\frac{\Gamma - {g_{2B}p_{2}^{\max}}}{g_{1B}},p_{1}^{\max}} \right\rbrack$

according to Table 1). Based on this deduction, we define the three new points, bounded by points X₀ and Y₀, as follows:

$O_{1}^{I{.1}} = \left\{ {\begin{matrix} \left( {p_{1}^{o_{1}},{f_{l_{0}}\left( p_{1}^{o_{1}} \right)}} \right) & {{{if}\mspace{14mu} \left( X_{0} \right)_{h}} < p_{1}^{o_{1}} < \left( Y_{0} \right)_{h}} \\ \varphi & {otherwise} \end{matrix};{O_{2}^{I{.1}} = \left\{ {\begin{matrix} \left( {p_{1}^{o_{2}},{f_{l_{0}}\left( p_{1}^{o_{2}} \right)}} \right) & {{{if}\mspace{14mu} \left( X_{0} \right)_{h}} < p_{1}^{o_{2}} < \left( Y_{0} \right)_{h}} \\ \varphi & {otherwise} \end{matrix};{O_{3}^{I{.1}} = \left\{ {\begin{matrix} \left( {p_{1}^{o_{3}},{f_{l_{0}}\left( p_{1}^{o_{3}} \right)}} \right) & {{{if}\mspace{14mu} \left( X_{0} \right)_{h}} < p_{1}^{o_{3}} < \left( Y_{0} \right)_{h}} \\ \varphi & {otherwise} \end{matrix}.} \right.}} \right.}} \right.$

Note that points X₀ and Y₀ are provided in Table 1, and p₁ ^(o) ¹ , p₁ ^(o) ² , and p₁ ^(o) ³ , are given in Lemma 2.

Using Lemma 1 and Lemma 2, we can derive (p*₁,p*₂) of Problem (P1) under Subcase I.1 as follows.

Theorem 1. (Optimal Power Allocation for Subcase I.1):

The optimal power allocations (p*₁,p*₂) of Problem (P1) under Subcase I.1 are:

$\left( {p_{1}^{*},p_{2}^{*}} \right) = \left\{ \begin{matrix} {\arg \; {\max\limits_{{({p_{1},p_{2}})} \in {\{{X_{0},X_{1},Y_{0},Y_{2},O_{1}^{I{.1}},O_{2}^{I{.1}}}\}}}\; {F\left( {p_{1},p_{2}} \right)}}} & {{{{if}\mspace{14mu} A} \neq 0},} \\ {\arg \; {\max\limits_{{({p_{1},p_{2}})} \in {\{{X_{0},X_{1},Y_{0},Y_{2},O_{3}^{I{.1}}}\}}}\; {F\left( {p_{1},p_{2}} \right)}}} & {{{if}\mspace{14mu} A} = 0.} \end{matrix} \right.$

Proof:

Please refer to Appendix III.

B.2 Optimal Power Allocations for Subcase I.2

Similar to Subcase I.1, we can also characterize the optimal power allocations of Problem (P1) under Subcase I.2. Specifically, we also define the following three points, which are bounded by points Z₁ and Z₂:

$O_{1}^{I{.2}} = \left\{ {\begin{matrix} \left( {p_{1}^{o_{1}},{f_{l_{0}}\left( p_{1}^{o_{1}} \right)}} \right) & {{{if}\mspace{14mu} \left( Z_{1} \right)_{h}} < p_{1}^{o_{1}} < \left( Z_{2} \right)_{h}} \\ \varphi & {otherwise} \end{matrix};{O_{2}^{I{.2}} = \left\{ {\begin{matrix} \left( {p_{1}^{o_{2}},{f_{l_{0}}\left( p_{1}^{o_{2}} \right)}} \right) & {{{if}\mspace{14mu} \left( Z_{1} \right)_{h}} < p_{1}^{o_{2}} < \left( Z_{2} \right)_{h}} \\ \varphi & {otherwise} \end{matrix};{O_{3}^{I{.2}} = \left\{ {\begin{matrix} \left( {p_{1}^{o_{3}},{f_{l_{0}}\left( p_{1}^{o_{3}} \right)}} \right) & {{{if}\mspace{14mu} \left( Z_{1} \right)_{h}} < p_{1}^{o_{3}} < \left( Z_{2} \right)_{h}} \\ \varphi & {otherwise} \end{matrix}.} \right.}} \right.}} \right.$

Note that p₁ ^(o) ¹ , p₁ ^(o) ² , and p₁ ^(o) ³ have been given in Lemma 2. However, different from Subcase I.1, Subcase I.2 (as shown in FIG. 2(b)), the line segment l₀ is constrained by points Z₁ and Z₂ (given in Table 1). Hence, the optimal power allocations of Problem (P1) under Subcase I.2 are as follows.

Theorem 2. (Optimal Power Allocation for Subcase I.2):

The optimal power allocations (p*₁,p*₂) of Problem (P1) under Subcase I.2 are:

$\left( {p_{1}^{*},p_{2}^{*}} \right) = \left\{ \begin{matrix} {\arg \; {\max\limits_{{({p_{1},p_{2}})} \in {\{{Z_{1},Z_{2},O_{1}^{I{.2}},O_{2}^{I{.2}}}\}}}\; {F\left( {p_{1},p_{2}} \right)}}} & {{{{if}\mspace{14mu} A} \neq 0},} \\ {\arg \; {\max\limits_{{({p_{1},p_{2}})} \in {\{{Z_{1},Z_{2},O_{3}^{I{.2}}}\}}}\; {F\left( {p_{1},p_{2}} \right)}}} & {{{if}\mspace{14mu} A} = 0.} \end{matrix} \right.$

Proof:

The proof is similar to that for Theorem 1 in Appendix III. It is easy for an ordinary skilled person to develop the proof by reference to Appendix III.

B.3 Optimal Power Allocations for Subcase I.3

Similar to Subcase I.1 and Subcase I.2, we define the following three points, which are bounded by points X₀ and Z₂ in Subcase I.3:

$O_{1}^{I{.3}} = \left\{ {\begin{matrix} \left( {p_{1}^{o_{1}},{f_{l_{0}}\left( p_{1}^{o_{1}} \right)}} \right) & {{{if}\mspace{14mu} \left( X_{0} \right)_{h}} < p_{1}^{o_{1}} < \left( Z_{2} \right)_{h}} \\ \varphi & {otherwise} \end{matrix};{O_{2}^{I{.3}} = \left\{ {\begin{matrix} \left( {p_{1}^{o_{2}},{f_{l_{0}}\left( p_{1}^{o_{2}} \right)}} \right) & {{{if}\mspace{14mu} \left( X_{0} \right)_{h}} < p_{1}^{o_{2}} < \left( Z_{2} \right)_{h}} \\ \varphi & {otherwise} \end{matrix};{O_{3}^{I{.3}} = \left\{ {\begin{matrix} \left( {p_{1}^{o_{3}},{f_{l_{0}}\left( p_{1}^{o_{3}} \right)}} \right) & {{{if}\mspace{14mu} \left( X_{0} \right)_{h}} < p_{1}^{o_{3}} < \left( Z_{2} \right)_{h}} \\ \varphi & {otherwise} \end{matrix}.} \right.}} \right.}} \right.$

Using the above-defined O₁ ^(I.3), O₂ ^(I.3), and O₃ ^(I.3), one can derive the optimal power allocations of Problem (P1) under Subcase I.3 as follows.

Theorem 3. (Optimal Power Allocation for Subcase I.3):

The optimal power allocations (p*₁,p*₂) of Problem (P1) under Subcase I.3 are:

$\left( {p_{1}^{*},p_{2}^{*}} \right) = \left\{ \begin{matrix} {\arg \; {\max\limits_{{({p_{1},p_{2}})} \in {\{{X_{0},X_{1},Z_{2},O_{1}^{I{.3}},O_{2}^{I{.3}}}\}}}\; {F\left( {p_{1},p_{2}} \right)}}} & {{{{if}\mspace{14mu} A} \neq 0},} \\ {\arg \; {\max\limits_{{({p_{1},p_{2}})} \in {\{{X_{0},X_{1},Z_{2},O_{3}^{I{.3}}}\}}}\; {F\left( {p_{1},p_{2}} \right)}}} & {{{if}\mspace{14mu} A} = 0.} \end{matrix} \right.$

Proof:

The proof is similar to that for Theorem 1 given in Appendix III.

B.4 Optimal Power Allocations for Subcase I.4

Similar to Subcases I.1, I.2, and I.3, we define the following three points, which are now bounded by points Z₂ and Y₀ in Subcase I.4:

$O_{1}^{I{.4}} = \left\{ {\begin{matrix} \left( {p_{1}^{o_{1}},{f_{l_{0}}\left( p_{1}^{o_{1}} \right)}} \right) & {{{if}\mspace{14mu} \left( Z_{1} \right)_{h}} < p_{1}^{o_{1}} < \left( Y_{0} \right)_{h}} \\ \varphi & {otherwise} \end{matrix};{O_{2}^{I{.4}} = \left\{ {\begin{matrix} \left( {p_{1}^{o_{2}},{f_{l_{0}}\left( p_{1}^{o_{2}} \right)}} \right) & {{{if}\mspace{14mu} \left( Z_{1} \right)_{h}} < p_{1}^{o_{2}} < \left( Y_{0} \right)_{h}} \\ \varphi & {otherwise} \end{matrix};{O_{3}^{I{.4}} = \left\{ {\begin{matrix} \left( {p_{1}^{o_{3}},{f_{l_{0}}\left( p_{1}^{o_{3}} \right)}} \right) & {{{if}\mspace{14mu} \left( Z_{1} \right)_{h}} < p_{1}^{o_{3}} < \left( Y_{0} \right)_{h}} \\ \varphi & {otherwise} \end{matrix}.} \right.}} \right.}} \right.$

Using the points O₁ ^(I.4), O₂ ^(I.4), and O₃ ^(I.4) defined above, we provide the following Theorem 4.

Theorem 4. (Optimal Power Allocation for Subcase I.4):

The optimal power allocations (p*₁,p*₂) of Problem (P1) under Subcase I.4 are:

$\left( {p_{1}^{*},p_{2}^{*}} \right) = \left\{ \begin{matrix} {\arg \; {\max\limits_{{({p_{1},p_{2}})} \in {\{{Y_{0},Y_{2},Z_{1},O_{1}^{I{.4}},O_{2}^{I{.4}}}\}}}\; {F\left( {p_{1},p_{2}} \right)}}} & {{{{if}\mspace{14mu} A} \neq 0},} \\ {\arg \; {\max\limits_{{({p_{1},p_{2}})} \in {\{{Y_{0},Y_{2},Z_{1},O_{3}^{I{.4}}}\}}}\; {F\left( {p_{1},p_{2}} \right)}}} & {{{if}\mspace{14mu} A} = 0.} \end{matrix} \right.$

Proof:

The proof is also similar to that for Theorem 1.

Summarizing the above four subcases, we finish deriving the optimal power allocations for Problem (P1) under Case I.

B.5 Optimal Power Allocations for Case II and Case III

We next derive the optimal power allocations under Case II and Case III by using the similar approach as that for Case I, i.e. categorizing different subcases that differentiate the location of l₀. In particular, we will show that each subcase of Case II (or Case III) is equivalent to one subcase of Case I, and thus the analytical optimal power allocations provided before in the previous Subsections A, B, C, and D are also applicable to the subcases of Case II and Case III.

Specifically, we first consider Case II, which includes three subcases, i.e. Subcase II.1 as shown in FIG. 1(b), Subcase II.2 in FIG. 3(a), and Subcase II.3 in FIG. 3(b), depending on how line l₀ locates with respect to points Y₁ and Y₂. (Different from that for Case I, the two points Y₁ and Y₂ in Case II have the same horizontal-coordinate, and thus there only exist three instead of four subcases in Case II.) In particular, a deep look at Subcase II.1 shows that it is in fact similar to Subcase I.4 (as shown in FIG. 2(d)), and the optimal power allocations in Theorem 4 is applicable. Subcase II.2 is similar to Subcase I.2 (in FIG. 2(b)), and thus the optimal power allocations in Theorem 2 is applicable. Finally, the optimal power allocations under Subcase II.3 are given by (p*₁,p*₂)=argmax_((p) ₁ _(,p) ₂ _()ε{Y) ₁ _(,Y) ₂ _(})F(p₁,p₂) according to Lemma 1. We thus finish deriving the optimal power allocations for Case II.

The results for Case II are as follows.

Subcase II.1: f_(l) _(o) ((Y₁)_(h))≧(Y₁)_(v) and f_(l) _(o) ((Y₂)_(h))≦(Y₂)_(v), meaning that point Y₁ is above line l₀ and point Y₂Y₂ is below l₀. The optimal power allocation for this subcase is completely similar to Theorem 4. (Notice that all of the points used therein are provided in Table I.)

Subcase II.2: f_(l) _(o) ((Y₁)_(h))≧(Y₁)_(v) and f_(l) _(o) ((Y₂)_(h))≧(Y₂)_(v), meaning that points Y₁ and Y₂ are both above line l₀. The optimal power allocation for this subcase is completely similar to Theorem 2.

Subcase II.3: f_(l) _(o) ((Y₁)_(h))≧(Y₁)_(v) and f_(l) _(o) ((Y₂)_(h))≧(Y₂)_(v), meaning that points Y₁ and Y₂ are both below line l₀. The optimal power allocation for this subcase is given by Theorem 5 below.

Theorem 5. (Optimal Power Allocation for Subcase II. 3):

The optimal power allocations (p*₁,p*₂) of Problem (P1) under Subcase II.3 are:

$\left( {p_{1}^{*},p_{2}^{*}} \right) = {\arg \; {\max\limits_{{({p_{1},p_{2}})} \in {\lbrack{Y_{1},Y_{2}}\rbrack}}\; {{F\left( {p_{1},p_{2}} \right)}.}}}$

Using the same approach as for Case II, an ordinary person skilled in the art can also derive the optimal power allocations for Case III (which in fact also has three different subcases). The results for Case III are as follows.

Subcase III.1: f_(l) _(o) ((X₁)_(h))≧(X₁)_(v) and f_(l) _(o) ((X₂)_(h))≦(X₂)_(v), meaning that point X₁ is below line l₀ and point X₂ is above l₀. The optimal power allocation for this subcase is completely similar to Theorem 3.

Subcase III.2: f_(l) _(o) ((X₁)_(h))≦(X₁)_(v) and f_(l) _(o) ((X₂)_(h))≦(X₂)_(v), meaning that points X₁ and X₂ are both above line l₀. The optimal power allocation for this subcase is completely similar to Theorem 2.

Subcase III.3: f_(l) _(o) ((X₁)_(h))≧(X₁)_(v) and f_(l) _(o) ((X₂)_(h))≧(X₂)_(v), meaning that points X₁ and X₂ are both below line l₀. The optimal power allocation for this subcase is given by Theorem 6 below.

Theorem 6. (Optimal Power Allocation for Subcase III.3):

The optimal power allocations (p*₁,p*₂) of Problem (P1) under Subcase III.3 are:

$\left( {p_{1}^{*},p_{2}^{*}} \right) = {\arg \; {\max\limits_{{({p_{1},p_{2}})} \in {\lbrack{X_{1},X_{2}}\rbrack}}\; {{F\left( {p_{1},p_{2}} \right)}.}}}$

B.6 Appendix I: Proof of Lemma 1

The proof is similar to that in [4], which considered the feasible region comprising each user's peak transmit power constraint, but without considering the PU's interference limit constraint (represented by line segment l₀ between points X₀ and Y₀ as shown in FIG. 2(a)). Thus, using the similar technique as in [4], we can prove that X₀, X₁, Y₀, and Y₂ are the candidates for the optimal power allocations of Problem (P1). Moreover, since (i) for some λ>1, there exists

$\begin{matrix} {{F\left( {{\lambda \; p_{1}},{\lambda \; p_{2}}} \right)} = {{\log_{2}\left( {1 + \frac{\lambda \; g_{11}p_{1}}{n_{1} + {\lambda \; g_{21}p_{2}}}} \right)} + {\log_{2}\left( {1 + \frac{\lambda \; g_{22}p_{2}}{n_{2} + {\lambda \; g_{12}p_{1}}}} \right)}}} \\ {= {{\log_{2}\left( {1 + \frac{\; {g_{11}p_{1}}}{\frac{n_{1}}{\lambda} + {g_{21}p_{2}}}} \right)} +}} \\ {{{{\log_{2}\left( {1 + \frac{\; {g_{22}p_{2}}}{\frac{n_{2}}{\lambda} + \; {g_{12}p_{1}}}} \right)} > {F\left( {p_{1},p_{2}} \right)}},}} \end{matrix}$

i.e. the total throughput of the two SUs increases when the power allocation pair (p₁,p₂) increases, and (ii) p₁ and p₂ are coupled by function g_(1B)p₁+g_(2B)p₂=Γ on line l₀. Therefore, the points on the line segment of l₀ between X₀ and Y₀ are also the possible candidates for the optimal power allocations.

Thus, the optimal power allocations of Problem (P1) under Subcase I.1 must reside within the vertices X₀, X₁, Y₀, and Y₂, or on the line segment of l₀ between points X₀ and Y₀.

B.7 Appendix II: Proof of Lemma 2

To derive the possible extremal points on line segment l₀ between points X₀ and Y₀, we calculate the first order derivative of {circumflex over (F)}(p₁) as follows:

${\frac{{\hat{F}\left( p_{1} \right)}}{p_{1}} = \frac{{Ap}_{1}^{2} + {Bp}_{1} + C}{D}},$

where parameters A, B, and C have given in (11), (12) and (13), respectively. Meanwhile, D is given by:

D=(n ₁ g _(2B) +g ₂₁ Γ+g ₁₁ g _(2B) p ₁ −g ₂₁ g _(1B) p ₁)(n ₁ g _(2B) +g ₂₁ Γ−g ₂₁ g _(1B) p ₁)×(n ₂ g _(1B) +g ₂₂ Γ+g ₁₂ g _(2B) p ₁ −g ₂₂ g _(1B) p ₁)×(n ₂ g _(2B) +g ₁₂ g _(2B) p ₁).

In particular, D>0 always holds, since the four items (in the product form) in D correspond to the numerators and denominators in the log(.) expression in (10). By imposing

${\frac{{\hat{F}\left( p_{1} \right)}}{p_{1}} = 0},$

we obtain a quadratic equation Ap₁ ²+Bp₁+C=0, whose roots can be analytically given by

${p_{1}^{o_{1}} = \frac{{–B} + \sqrt{B^{2} - {4{AC}}}}{2A}},{{{and}\mspace{14mu} p_{1}^{o_{2}}} = \frac{{–B} - \sqrt{B^{2} - {4{AC}}}}{2A}},\mspace{11mu} {{{{if}\mspace{14mu} A} \neq 0};}$ $\mspace{79mu} {{p_{1}^{o_{3}} = \; {- \frac{C}{B}}},{{{if}\mspace{14mu} A} = 0},}$

which correspond to the extremal points on line segment l₀ that might maximize {circumflex over (F)}(p₁) (or equivalently, F(p₁,p₂)).

B.8 Appendix III: Proof of Theorem 3

For the case of A≠0, there exist two extremal points O₁ ^(I.1)=(p₁ ^(o) ¹ ,f_(l) ₀ (p₁ ^(o) ¹ )), O₂ ^(I.1)=(p₁ ^(o) ² ,f_(l) ₀ (p₁ ^(o) ² )) on the line segment of l₀ according to Lemma 2. In companion with the previous four points X₀,X₁,Y₀, and Y₂ (which have been proved in Lemma 1), the optimal power allocations (p*₁,p*₂) of Problem (P1) under Subcase I.1 can thus be given by

$\left( {p_{1}^{*},p_{2}^{*}} \right) = {\arg \; {\max\limits_{{({p_{1},p_{2}})} \in {\{{X_{0},X_{1},{Y_{0}Y_{2}},O_{1}^{I,1},O_{2}^{I,1}}\}}}{{F\left( {p_{1},p_{2}} \right)}.}}}$

Similarly, for the case of A=0, there exists one extremal point O₃ ^(I.1)=(p₁ ^(o) ³ , f_(l) ₀ (p₁ ^(o) ³ )) on the line segment of l₀. Again, in companion with the previous four points X₀,X₁,Y₀, and Y₂, the optimal power allocations (p*₁,p*₂) under Subcase I.1 can thus be given by

$\left( {p_{1}^{*},p_{2}^{*}} \right) = {\arg \; {\max\limits_{{({p_{1},p_{2}})} \in {\{{X_{0},X_{1},{Y_{0}Y_{2}},O_{3}^{I,1}}\}}}{{F\left( {p_{1},p_{2}} \right)}.}}}$

C. Numerical Results

In this section, we perform numerical simulations to validate our analytical results in Theorems 1, 2, 3, and 4. In particular, we focus on presenting the results that the optimal power allocations (p*₁,p*₂) occur on line l₀ when the PU's interference limit is binding. That is, one of the extremal points given in Lemma 2 turns out to be the optimal power allocations, which is a key advance we have made in this work in comparison with the previous ones. We set the background noise n_(i)=0.01 W and set the SUs' maximum transmit powers p₁ ^(max)=1.0 W and p₂ ^(max)=1.5 W.

FIG. 4 shows the values of F(p₁,p₂) by enumerating different (p₁,p₂) for three cases of the PU's interference limit Γ (i.e., Γ=0.1, 0.15, and 0.2). In this FIG., we set g₁₁=0.2, g₂₂=0.4, g₁₂=g₂₁=0.01, g_(1B)=0.15, g_(2B)=0.1, and γ₁=γ₂=2, which yield A≠0 according to (11). In each subfigure thereof, we mark out the optimal power allocations (p*₁,p*₂) (denoted by “Ana:”), which are analytically derived according to one of Theorems 1, 2, 3, and 4. In particular, only one theorem is applicable to derive (p*₁,p*₂), depending on which of the specific cases (i.e. Subcases I.1, I.2, I.3, and I.4) occurs. For instance, Subcase I.2 occurs in FIG. 3(a), and we thus use Theorem 2 to derive (p₁,p₂) accordingly. To validate this result, we also use exhaustive search to find the optimal power allocations (in a brute-force manner) and mark out the corresponding result which is denoted by “Ex:”. The comparisons between the two sets of the results show that the analytically derived (p*₁,p*₂) match those obtained by exhaustive search very well, thus validating Theorems 1, 2, 3, and 4. Besides, in each subfigure, we also mark out the SUs' total interference, i.e. l=p*₁g_(1B)+p*₂g_(2B), which matches with Γ well, meaning that the optimal power allocations (p*₁,p*₂) indeed occur on line l₀ (i.e., the PU's interference limit constraint is strictly binding). Moreover, the comparisons among the three subfigures show that when the PU's interference limit increases, the optimal power allocations (p*₁,p*₂) increase accordingly. That is, the SUs are able to transmit more aggressively and achieve a higher throughput.

FIG. 5 shows F(p₁,p₂) with different levels of the cross interference (i.e. g₁₂=g₂₁=0.01, 0.02 and 0.03 in the three subfigures, respectively). Here, we set g₁₁=0.2, g₂₂=0.4, g_(1B)=0.15, g_(2B)=0.1, mma₁=γ₂=2, and Γ=0.1, which again yield A≠0 according to (11). Similar to FIG. 1, the analytically derived optimal power allocations (p*₁,p*₂) based on Theorems 1, 2, 3, and 4 match those obtained by exhaustive search very well. Besides, the optimal power allocations (p*₁,p*₂) indeed occur when the PU's interference limit constraint is binding. In particular, the comparison among the three subfigures shows that when the cross interference is weak, both SUs can transmit simultaneously (i.e. sharing the PU's spectrum) for maximizing their total throughput. In comparison, when the cross interference becomes greater, only one favorable SU will be selected to transmit (to avoid excessive interference between the SUs) for maximizing their total throughput. This in fact turns back to the results in the previous work. That is, the optimal power allocations occur on one of the vertices of the feasible region.

Finally, FIG. 1 also shows F(p₁,p₂) for the case of A=0 (in particular, we set a symmetric case that g₁₁=g₂₂=0.2, g₁₂=g₂₁=0.02, g_(1B)=g_(2B)=0.1, and γ₁=γ₂=2, which yield A=0 according to (11)). We also vary Γ=0.1, 0.15, and 0.2 in the three subfigures. Again, in all the tested cases, the analytically-derived optimal power allocations match those obtained by exhaustive search, thus validating Theorems 1, 2, 3, and 4 for the case of A=0.

D. The Present Invention

The present invention is developed based on the optimal power allocation scheme as disclosed above.

An aspect of the present invention is to provide a method of power control for a plurality of SUs in a cognitive radio network having a plurality of PUs. In the method, an optimal power level for each of the SUs is determined such that a total throughput of all the SUs is maximized while satisfying an individual throughput requirement of each SU and an interference limit constraint of the PUs.

In one embodiment, the number of SUs is two, and the optimal power level for each of the SUs is determined by maximizing a two-user throughput as defined in P1 subject to constraints given by (1)-(4).

Preferably, the maximizing of the two-user throughput comprises determining feasibility of maximizing the throughput under the constraints by checking if (9) is met.

It is also preferable that the maximizing of the two-user throughput further comprises the following steps. First, lines according to (5)-(8) are constructed. If the feasibility of maximizing the throughput is confirmed, a feasible region being enclosed by the constructed lines is identified. Then coordinates of critical points, as mentioned above and shown in Table I, are determined such that the optimal power level for each of the SUs is determined according to the coordinates of the critical points.

Advantageously, the two-user throughput is maximized according to Theorems 1-6 for Subcases I.1-4, Subcases II.1-3 and Subcases III.1-3.

The embodiments disclosed herein may be implemented using general purpose or specialized computing devices, computer processors, or electronic circuitries including but not limited to digital signal processors, application specific integrated circuits, field programmable gate arrays, and other programmable logic devices configured or programmed according to the teachings of the present disclosure. Computer instructions or software codes running in the general purpose or specialized computing devices, computer processors, or programmable logic devices can readily be prepared by practitioners skilled in the software or electronic art based on the teachings of the present disclosure.

The present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiment is therefore to be considered in all respects as illustrative and not restrictive. The scope of the invention is indicated by the appended claims rather than by the foregoing description, and all changes that come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. 

What is claimed is:
 1. A method of power control for a plurality of secondary users (SUs) in a cognitive radio network having a plurality of primary users (PUs), the method comprising: determining an optimal power level for each of the SUs such that a total throughput of all the SUs is maximized while satisfying an individual throughput requirement of each SU and an interference limit constraint of the PUs.
 2. The method of claim 1, wherein the number of SUs is two, and wherein the optimal power level for each of the SUs is determined by maximizing a two-user throughput of ${\max \; {F\left( {p_{1},p_{2}} \right)}} = {{W\; {\log_{2}\left( {1 + \frac{p_{1}g_{11}}{n_{1} + {p_{2}g_{21}}}} \right)}} + {W\; {\log_{2}\left( {1 + \frac{p_{2}g_{22}}{n_{2} + {p_{1}g_{12}}}} \right)}}}$ subject to constraints: p₁g_(1B) + p₂g_(2B) ≤ Γ; ${\frac{p_{1}g_{11}}{n_{1} + {p_{2}g_{21}}} \geq \gamma_{1}};$ ${\frac{p_{2}g_{22}}{n_{2} + {p_{1}g_{12}}} \geq \gamma_{2}};{and}$ 0 ≤ p₁ ≤ p₁^(m ax)  and  0 ≤ p₂ ≤ p₂^(ma x); where: p_(i) denotes the transmit power of SU i; g_(ii) denotes a channel power gain from a transmitter of SU i to its receiver; g_(ij) denotes a channel power gain from the transmitter of SU i to a receiver of SU j; g_(iB) denotes a channel power gain from the transmitter of SU i to a base station (BS); n_(i) denotes the power of background noise experienced by the receiver of SU i; p_(i) ^(max) denotes the upper bound that each SU i's transmit power cannot exceed; Γ is an interference limit of each of the PUs; and γ_(i) is a signal to interference plus noise ratio required to achieve the individual throughput requirement of SU i.
 3. The method of claim 2, wherein the maximizing of the two-user throughput comprises: determining feasibility of maximizing the throughput under the constraints by checking if a condition g₁₁g₂₂−γ₁γ₂g₁₂g₂₁>0 is met.
 4. The method of claim 3, wherein the maximizing of the two-user throughput further comprises: constructing lines: ${{{Line}\mspace{14mu} l_{0}\text{:}\mspace{14mu} p_{2}} = {{f_{l_{0}}\left( p_{1} \right)} = {{{- \frac{g_{1B}}{g_{2B}}}p_{1}} + \frac{\Gamma}{g_{2B}}}}};$ ${{{Line}\mspace{14mu} l_{1}\text{:}\mspace{14mu} p_{2}} = {{f_{l_{1}}\left( p_{1} \right)} = {{{- \frac{g_{11}}{\gamma_{1}g_{21}}}p_{1}} - \frac{n_{1}}{g_{21}}}}};$ ${{{Line}\mspace{14mu} l_{2}\text{:}\mspace{14mu} p_{2}} = {{f_{l_{2}}\left( p_{1} \right)} = {{\frac{\gamma_{2}g_{12}}{g_{22}}p_{1}} + \frac{\gamma_{2}n_{2}}{g_{22}}}}};$ Line  l_(m 1):  p₁ = p₁^(ma x); and Line  l_(m 2):  p₂ = p₂^(ma x); when the feasibility of maximizing the throughput is confirmed, identifying a feasible region being enclosed by the constructed lines; and determining coordinates of critical points such that the optimal power level for each of the SUs is determined according to the coordinates of the critical points.
 5. The method according to claim 4, wherein the maximizing of the two-user throughput further comprises: when f_(l) ₀ ((X₁)_(h))≧(X₁)_(v) and f_(l) ₀ ((Y₂)_(h))≧(Y₂)_(v), computing the optimal power levels of SU 1 and SU 2, respectively denoted as p*₁ and p*₂, for maximizing the two-user throughput by $\mspace{20mu} {\left( {p_{1}^{*},p_{2}^{*}} \right) = \left\{ {\begin{matrix} {\arg \; {\max\limits_{{({p_{1},p_{2}})} \in {\{{X_{0},X_{1},Y_{0},Y_{2},O_{1}^{I,1},O_{2}^{I,1}}\}}}{F\left( {p_{1},p_{2}} \right)}}} & {{{{if}\mspace{14mu} A} \neq 0},} \\ {\arg \; {\max\limits_{{({p_{1},p_{2}})} \in {\{{X_{0},X_{1},Y_{0},Y_{2},O_{3}^{I,1}}\}}}{F\left( {p_{1},p_{2}} \right)}}} & {{{if}\mspace{14mu} A} = 0} \end{matrix};\mspace{20mu} {{{where}\text{:}\mspace{20mu} X_{0}} = {{\left( {\frac{\Gamma - {g_{2B}p_{2}^{{ma}\; x}}}{g_{1B}},p_{2}^{{ma}\; x}} \right)\mspace{14mu} {and}\mspace{14mu} X_{0}} = \left( {\left( X_{0} \right)_{h},\left( X_{0} \right)_{v}} \right)}};\mspace{20mu} {X_{1} = {{\left( {\frac{{\gamma_{1}n_{1}} + {g_{21}\gamma_{1}p_{2}^{{ma}\; x}}}{g_{11}},p_{2}^{{ma}\; x}} \right)\mspace{14mu} {and}\mspace{14mu} X_{1}} = \left( {\left( X_{1} \right)_{h},\left( X_{1} \right)_{v}} \right)}};\mspace{20mu} {Y_{0} = {{\left( {p_{1}^{m\; {ax}},\frac{\Gamma - {g_{1B}p_{1}^{{ma}\; x}}}{g_{2B}\;}} \right)\mspace{14mu} {and}\mspace{14mu} Y_{0}} = \left( {\left( Y_{0} \right)_{h},\left( Y_{0} \right)_{v}} \right)}};\mspace{20mu} {Y_{2} = {{\left( {p_{1}^{{ma}\; x},\frac{{n_{2}\gamma_{2}} + {g_{12}\gamma_{2}p_{1}^{{ma}\; x}}}{g_{22}}} \right)\mspace{14mu} {and}\mspace{14mu} Y_{2}} = \left( {\left( Y_{2} \right)_{h},\left( Y_{2} \right)_{v}} \right)}};\mspace{20mu} {O_{1}^{{I,1}\;} = \left\{ {\begin{matrix} \left( {p_{1}^{o_{1}},{f_{l_{0}}\left( p_{1}^{o_{1}} \right)}} \right) & {{{if}\mspace{14mu} \left( X_{0} \right)_{h}} < p_{1}^{o_{1}} < \left( Y_{0} \right)_{h}} \\ \varphi & {otherwise} \end{matrix};\mspace{20mu} {O_{2}^{I,1} = \left\{ {\begin{matrix} \left( {p_{1}^{o_{2}},{f_{l_{0}}\left( p_{1}^{o_{2}} \right)}} \right) & {{{if}\mspace{14mu} \left( X_{0} \right)_{h}} < p_{1}^{o_{2}} < \left( Y_{0} \right)_{h}} \\ \varphi & {otherwise} \end{matrix};\mspace{20mu} {O_{3}^{I,1} = \left\{ {\begin{matrix} \left( {p_{1}^{o_{3}},{f_{l_{0}}\left( p_{1}^{o_{3}} \right)}} \right) & {{{if}\mspace{14mu} \left( X_{0} \right)_{h}} < p_{1}^{o_{3}} < \left( Y_{0} \right)_{h}} \\ \varphi & {otherwise} \end{matrix};\mspace{20mu} {p_{1}^{o_{1}} = {{\frac{{- B} + \sqrt{B^{2} - {4A\; C}}}{2A}\mspace{14mu} {if}\mspace{14mu} A} \neq 0}};\mspace{20mu} {p_{1}^{o_{2}} = {{\frac{{- B} - \sqrt{B^{2} - {4A\; C}}}{2A}\mspace{11mu} {if}\mspace{14mu} A} \neq 0}};\mspace{20mu} {p_{1}^{o_{3}} = {{\frac{- C}{B}\mspace{14mu} {if}\mspace{14mu} A} = 0}};{A = {\frac{g_{2B}}{\ln \; 2}\left\lbrack {{g_{11}g_{12}{g_{2B}\left( {{n_{1}g_{2B}} + {g_{21}\Gamma}} \right)}\left( {{g_{12}g_{2B}} - {g_{22}g_{1B}}} \right)} - {g_{22}g_{21}{g_{1B}\left( {{g_{1B}n_{2}} + {g_{12}\Gamma}} \right)}\left( {{g_{21}g_{1B}} - {g_{11}g_{2B}}} \right)}} \right\rbrack}};{B = {\frac{g_{2B}\left( {{n_{1}g_{2B}} + {g_{21}\Gamma}} \right)}{\ln \; 2} \times \left\lbrack {{g_{11}{g_{2B}\left( {{2g_{12}g_{2B}n_{2}} + {g_{12}g_{22}\Gamma} - {g_{22}g_{1B}n_{2}}} \right)}} - {{g_{22}\left( {{g_{1B}n_{2}} + {g_{12}\Gamma}} \right)}\left( {{g_{11}g_{2B}} - {2g_{21}g_{1B}}} \right)}} \right\rbrack}};{C = {\frac{g_{2B}\left( {{n_{1}g_{2B}} + {g_{21}\Gamma}} \right)}{\ln \; 2} \times {\left\lbrack {{g_{11}g_{2B}{n_{2}\left( {{n_{2}g_{2B}} + {g_{22}\Gamma}} \right)}} - {{g_{22}\left( {{g_{1B}n_{2}} + {g_{12}\Gamma}} \right)}\left( {{n_{1}g_{2B}} + {g_{21}\Gamma}} \right)}} \right\rbrack.}}}} \right.}} \right.}} \right.}} \right.}$
 6. The method according to claim 4, wherein the maximizing of the two-user throughput further comprises: when f_(l) ₀ ((X₁)_(h))≦(X₁)_(v) and f_(l) ₀ ((Y₂)_(h))≦(Y₂)_(v), computing the optimal power levels of SU 1 and SU 2, respectively denoted as p*₁ and p*₂, for maximizing the two-user throughput by $\mspace{20mu} {\left( {p_{1}^{*},p_{2}^{*}} \right) = \left\{ {\begin{matrix} {\arg \; {\max\limits_{{({p_{1},p_{2}})} \in {\{{Z_{1},Z_{2},O_{1}^{I,2},O_{2}^{I,2}}\}}}{F\left( {p_{1},p_{2}} \right)}}} & {{{{if}\mspace{14mu} A} \neq 0},} \\ {\arg \; {\max\limits_{{({p_{1},p_{2}})} \in {\{{Z_{1},Z_{2},O_{3}^{I,2}}\}}}{F\left( {p_{1},p_{2}} \right)}}} & {{{if}\mspace{14mu} A} = 0} \end{matrix};\mspace{20mu} {{{where}\text{:}\mspace{20mu} X_{1}} = {{\left( {\frac{{\gamma_{1}n_{1}} + {g_{21}\gamma_{1}p_{2}^{{ma}\; x}}}{g_{11}},p_{2}^{{ma}\; x}} \right)\mspace{14mu} {and}\mspace{14mu} X_{1}} = \left( {\left( X_{1} \right)_{h},\left( X_{1} \right)_{v}} \right)}};\mspace{20mu} {Y_{2} = {{\left( {p_{1}^{{ma}\; x},\frac{{n_{2}\gamma_{2}} + {g_{12}\gamma_{2}p_{1}^{{ma}\; x}}}{g_{22}}} \right)\mspace{14mu} {and}\mspace{14mu} Y_{2}} = \left( {\left( Y_{2} \right)_{h},\left( Y_{2} \right)_{v}} \right)}};\mspace{20mu} {Z_{1} = {{\left( {\frac{{g_{21}\gamma_{1}\Gamma} + {g_{2B}n_{1}\gamma_{1}}}{{g_{11}g_{2B}} + {g_{1B}g_{21}\gamma_{1}}},\frac{{g_{11}\Gamma} - {g_{1B}n_{1}\gamma_{1}}}{{g_{11}g_{2B}} + {g_{1B}g_{21}\gamma_{1}}}} \right)\mspace{20mu} {and}\mspace{14mu} Z_{1}} = \left( {\left( Z_{1} \right)_{h},\left( Z_{1} \right)_{v}} \right)}};\mspace{20mu} {Z_{2} = {{\left( {\frac{{g_{22}\Gamma} - {g_{2B}n_{2}\gamma_{2}}}{{g_{22}g_{1B}} + {g_{12}g_{12}\gamma_{2}}},\frac{{g_{12}\gamma_{2}\Gamma} + {g_{1B}n_{2}\gamma_{2}}}{{g_{22}g_{1B}} + {g_{2B}g_{12}\gamma_{2}}}} \right)\mspace{20mu} {and}\mspace{14mu} Z_{2}} = \left( {\left( Z_{2} \right)_{h},\left( Z_{2} \right)_{v}} \right)}};\mspace{20mu} {O_{1}^{{I,2}\;} = \left\{ {\begin{matrix} \left( {p_{1}^{o_{1}},{f_{l_{0}}\left( p_{1}^{o_{1}} \right)}} \right) & {{{if}\mspace{14mu} \left( Z_{1} \right)_{h}} < p_{1}^{o_{1}} < \left( Z_{2} \right)_{h}} \\ \varphi & {otherwise} \end{matrix};\mspace{20mu} {O_{2}^{I,2} = \left\{ {\begin{matrix} \left( {p_{1}^{o_{2}},{f_{l_{0}}\left( p_{1}^{o_{2}} \right)}} \right) & {{{if}\mspace{14mu} \left( Z_{1} \right)_{h}} < p_{1}^{o_{2}} < \left( Z_{2} \right)_{h}} \\ \varphi & {otherwise} \end{matrix};\mspace{20mu} {O_{3}^{I,2} = \left\{ {\begin{matrix} \left( {p_{1}^{o_{3}},{f_{l_{0}}\left( p_{1}^{o_{3}} \right)}} \right) & {{{if}\mspace{14mu} \left( Z_{1} \right)_{h}} < p_{1}^{o_{3}} < \left( Z_{2} \right)_{h}} \\ \varphi & {otherwise} \end{matrix};\mspace{20mu} {p_{1}^{o_{1}} = {{\frac{{- B} + \sqrt{B^{2} - {4A\; C}}}{2A}\mspace{14mu} {if}\mspace{14mu} A} \neq 0}};\mspace{20mu} {p_{1}^{o_{2}} = {{\frac{{- B} - \sqrt{B^{2} - {4A\; C}}}{2A}\mspace{11mu} {if}\mspace{14mu} A} \neq 0}};\mspace{20mu} {p_{1}^{o_{3}} = {{\frac{- C}{B}\mspace{14mu} {if}\mspace{14mu} A} = 0}};{A = {\frac{g_{2B}}{\ln \; 2}\left\lbrack {{g_{11}g_{12}{g_{2B}\left( {{n_{1}g_{2B}} + {g_{21}\Gamma}} \right)}\left( {{g_{12}g_{2B}} - {g_{22}g_{1B}}} \right)} - {g_{22}g_{21}{g_{1B}\left( {{g_{1B}n_{2}} + {g_{12}\Gamma}} \right)}\left( {{g_{21}g_{1B}} - {g_{11}g_{2B}}} \right)}} \right\rbrack}};{B = {\frac{g_{2B}\left( {{n_{1}g_{2B}} + {g_{21}\Gamma}} \right)}{\ln \; 2} \times \left\lbrack {{g_{11}{g_{2B}\left( {{2g_{12}g_{2B}n_{2}} + {g_{12}g_{22}\Gamma} - {g_{22}g_{1B}n_{2}}} \right)}} - {{g_{22}\left( {{g_{1B}n_{2}} + {g_{12}\Gamma}} \right)}\left( {{g_{11}g_{2B}} - {2g_{21}g_{1B}}} \right)}} \right\rbrack}};{C = {\frac{g_{2B}\left( {{n_{1}g_{2B}} + {g_{21}\Gamma}} \right)}{\ln \; 2} \times {\left\lbrack {{g_{11}g_{2B}{n_{2}\left( {{n_{2}g_{2B}} + {g_{22}\Gamma}} \right)}} - {{g_{22}\left( {{g_{1B}n_{2}} + {g_{12}\Gamma}} \right)}\left( {{n_{1}g_{2B}} + {g_{21}\Gamma}} \right)}} \right\rbrack.}}}} \right.}} \right.}} \right.}} \right.}$
 7. The method according to claim 4, wherein the maximizing of the two-user throughput further comprises: when f_(l) ₀ ((X₁)_(h))≧(X₁)_(v) and f_(l) ₀ ((Y₂)_(h))≦(Y₂)_(v), computing the optimal power levels of SU 1 and SU 2, respectively denoted as p*₁ and p*₂, for maximizing the two-user throughput by $\mspace{20mu} {\left( {p_{1}^{*},p_{2}^{*}} \right) = \left\{ {\begin{matrix} {\arg \; {\max\limits_{{({p_{1},p_{2}})} \in {\{{X_{0},X_{1},Z_{2},O_{1}^{I,3},O_{2}^{I,3}}\}}}{F\left( {p_{1},p_{2}} \right)}}} & {{{{if}\mspace{14mu} A} \neq 0},} \\ {\arg \; {\max\limits_{{({p_{1},p_{2}})} \in {\{{X_{0},X_{1},Z_{2},O_{2}^{I,3}}\}}}{F\left( {p_{1},p_{2}} \right)}}} & {{{if}\mspace{14mu} A} = 0} \end{matrix};\mspace{20mu} {{{where}\text{:}\mspace{20mu} X_{0}} = {{\left( {\frac{\Gamma - {g_{2B}p_{2}^{{ma}\; x}}}{g_{1B}},p_{2}^{{ma}\; x}} \right)\mspace{14mu} {and}\mspace{14mu} X_{0}} = \left( {\left( X_{0} \right)_{h},\left( X_{0} \right)_{v}} \right)}};\mspace{20mu} {X_{1} = {{\left( {\frac{{\gamma_{1}n_{1}} + {g_{21}\gamma_{1}p_{2}^{{ma}\; x}}}{g_{11}},p_{2}^{{ma}\; x}} \right)\mspace{14mu} {and}\mspace{14mu} X_{1}} = \left( {\left( X_{1} \right)_{h},\left( X_{1} \right)_{v}} \right)}};\mspace{20mu} {Y_{2} = {{\left( {p_{1}^{{ma}\; x},\frac{{n_{2}\gamma_{2}} + {g_{12}\gamma_{2}p_{1}^{{ma}\; x}}}{g_{22}}} \right)\mspace{14mu} {and}\mspace{14mu} Y_{2}} = \left( {\left( Y_{2} \right)_{h},\left( Y_{2} \right)_{v}} \right)}};\mspace{20mu} {Z_{2} = {{\left( {\frac{{g_{22}\Gamma} - {g_{2B}n_{2}\gamma_{2}}}{{g_{22}g_{1B}} + {g_{12}g_{12}\gamma_{2}}},\frac{{g_{12}\gamma_{2}\Gamma} + {g_{1B}n_{2}\gamma_{2}}}{{g_{22}g_{1B}} + {g_{2B}g_{12}\gamma_{2}}}} \right)\mspace{20mu} {and}\mspace{14mu} Z_{2}} = \left( {\left( Z_{2} \right)_{h},\left( Z_{2} \right)_{v}} \right)}};\mspace{20mu} {O_{1}^{{I,3}\;} = \left\{ {\begin{matrix} \left( {p_{1}^{o_{1}},{f_{l_{0}}\left( p_{1}^{o_{1}} \right)}} \right) & {{{if}\mspace{14mu} \left( X_{0} \right)_{h}} < p_{1}^{o_{1}} < \left( Z_{2} \right)_{h}} \\ \varphi & {otherwise} \end{matrix};\mspace{20mu} {O_{2}^{I,3} = \left\{ {\begin{matrix} \left( {p_{1}^{o_{2}},{f_{l_{0}}\left( p_{1}^{o_{2}} \right)}} \right) & {{{if}\mspace{14mu} \left( X_{0} \right)_{h}} < p_{1}^{o_{2}} < \left( Z_{2} \right)_{h}} \\ \varphi & {otherwise} \end{matrix};\mspace{20mu} {O_{3}^{I,3} = \left\{ {\begin{matrix} \left( {p_{1}^{o_{3}},{f_{l_{0}}\left( p_{1}^{o_{3}} \right)}} \right) & {{{if}\mspace{14mu} \left( X_{0} \right)_{h}} < p_{1}^{o_{3}} < \left( Z_{2} \right)_{h}} \\ \varphi & {otherwise} \end{matrix};\mspace{20mu} {p_{1}^{o_{1}} = {{\frac{{- B} + \sqrt{B^{2} - {4A\; C}}}{2A}\mspace{14mu} {if}\mspace{14mu} A} \neq 0}};\mspace{20mu} {p_{1}^{o_{2}} = {{\frac{{- B} - \sqrt{B^{2} - {4A\; C}}}{2A}\mspace{11mu} {if}\mspace{14mu} A} \neq 0}};\mspace{20mu} {p_{1}^{o_{3}} = {{\frac{- C}{B}\mspace{14mu} {if}\mspace{14mu} A} = 0}};{A = {\frac{g_{2B}}{\ln \; 2}\left\lbrack {{g_{11}g_{12}{g_{2B}\left( {{n_{1}g_{2B}} + {g_{21}\Gamma}} \right)}\left( {{g_{12}g_{2B}} - {g_{22}g_{1B}}} \right)} - {g_{22}g_{21}{g_{1B}\left( {{g_{1B}n_{2}} + {g_{12}\Gamma}} \right)}\left( {{g_{21}g_{1B}} - {g_{11}g_{2B}}} \right)}} \right\rbrack}};{B = {\frac{g_{2B}\left( {{n_{1}g_{2B}} + {g_{21}\Gamma}} \right)}{\ln \; 2} \times \left\lbrack {{g_{11}{g_{2B}\left( {{2g_{12}g_{2B}n_{2}} + {g_{12}g_{22}\Gamma} - {g_{22}g_{1B}n_{2}}} \right)}} - {{g_{22}\left( {{g_{1B}n_{2}} + {g_{12}\Gamma}} \right)}\left( {{g_{11}g_{2B}} - {2g_{21}g_{1B}}} \right)}} \right\rbrack}};{C = {\frac{g_{2B}\left( {{n_{1}g_{2B}} + {g_{21}\Gamma}} \right)}{\ln \; 2} \times {\left\lbrack {{g_{11}g_{2B}{n_{2}\left( {{n_{2}g_{2B}} + {g_{22}\Gamma}} \right)}} - {{g_{22}\left( {{g_{1B}n_{2}} + {g_{12}\Gamma}} \right)}\left( {{n_{1}g_{2B}} + {g_{21}\Gamma}} \right)}} \right\rbrack.}}}} \right.}} \right.}} \right.}} \right.}$
 8. The method according to claim 4, wherein the maximizing of the two-user throughput further comprises: when f_(l) ₀ ((X₁)_(h))≦(X₁)_(v) and f_(l) ₀ ((Y₂)_(h))≧(Y₂)_(v), computing the optimal power levels of SU 1 and SU 2, respectively denoted as p*₁ and p*₂, for maximizing the two-user throughput by $\mspace{20mu} {\left( {p_{1}^{*},p_{2}^{*}} \right) = \left\{ {\begin{matrix} {\arg \; {\max\limits_{{({p_{1},p_{2}})} \in {\{{Y_{0},Y_{2},O_{1}^{I,4},O_{2}^{I,4}}\}}}{F\left( {p_{1},p_{2}} \right)}}} & {{{{if}\mspace{14mu} A} \neq 0},} \\ {\arg \; {\max\limits_{{({p_{1},p_{2}})} \in {\{{Y_{0},Y_{2},O_{3}^{I,4}}\}}}{F\left( {p_{1},p_{2}} \right)}}} & {{{if}\mspace{14mu} A} = 0} \end{matrix};\mspace{20mu} {{{where}\text{:}\mspace{20mu} X_{1}} = {{\left( {\frac{{\gamma_{1}n_{1}} + {g_{21}\gamma_{1}p_{2}^{{ma}\; x}}}{g_{11}},p_{2}^{{ma}\; x}} \right)\mspace{14mu} {and}\mspace{14mu} X_{1}} = \left( {\left( X_{1} \right)_{h},\left( X_{1} \right)_{v}} \right)}};\mspace{20mu} {Y_{0} = {{\left( {p_{1}^{m\; {ax}},\frac{\Gamma - {g_{1B}p_{1}^{{ma}\; x}}}{g_{2B}\;}} \right)\mspace{14mu} {and}\mspace{14mu} Y_{0}} = \left( {\left( Y_{0} \right)_{h},\left( Y_{0} \right)_{v}} \right)}};\mspace{20mu} {Y_{2} = {{\left( {p_{1}^{{ma}\; x},\frac{{n_{2}\gamma_{2}} + {g_{12}\gamma_{2}p_{1}^{{ma}\; x}}}{g_{22}}} \right)\mspace{14mu} {and}\mspace{14mu} Y_{2}} = \left( {\left( Y_{2} \right)_{h},\left( Y_{2} \right)_{v}} \right)}};\mspace{20mu} {Z_{1} = {{\left( {\frac{{g_{21}\gamma_{1}\Gamma} + {g_{2B}n_{1}\gamma_{1}}}{{g_{11}g_{2B}} + {g_{1B}g_{21}\gamma_{1}}},\frac{{g_{11}\Gamma} - {g_{1B}n_{1}\gamma_{1}}}{{g_{11}g_{2B}} + {g_{1B}g_{21}\gamma_{1}}}} \right)\mspace{20mu} {and}\mspace{14mu} Z_{1}} = \left( {\left( Z_{1} \right)_{h},\left( Z_{1} \right)_{v}} \right)}};\mspace{20mu} {O_{1}^{{I,4}\;} = \left\{ {\begin{matrix} \left( {p_{1}^{o_{1}},{f_{l_{0}}\left( p_{1}^{o_{1}} \right)}} \right) & {{{if}\mspace{14mu} \left( Z_{1} \right)_{h}} < p_{1}^{o_{1}} < \left( Y_{0} \right)_{h}} \\ \varphi & {otherwise} \end{matrix};\mspace{20mu} {O_{2}^{I,4} = \left\{ {\begin{matrix} \left( {p_{1}^{o_{2}},{f_{l_{0}}\left( p_{1}^{o_{2}} \right)}} \right) & {{{if}\mspace{14mu} \left( Z_{1} \right)_{h}} < p_{1}^{o_{2}} < \left( Y_{0} \right)_{h}} \\ \varphi & {otherwise} \end{matrix};\mspace{20mu} {O_{3}^{I,4} = \left\{ {\begin{matrix} \left( {p_{1}^{o_{3}},{f_{l_{0}}\left( p_{1}^{o_{3}} \right)}} \right) & {{{if}\mspace{14mu} \left( Z_{1} \right)_{h}} < p_{1}^{o_{3}} < \left( Y_{0} \right)_{h}} \\ \varphi & {otherwise} \end{matrix};\mspace{20mu} {p_{1}^{o_{1}} = {{\frac{{- B} + \sqrt{B^{2} - {4A\; C}}}{2A}\mspace{14mu} {if}\mspace{14mu} A} \neq 0}};\mspace{20mu} {p_{1}^{o_{2}} = {{\frac{{- B} - \sqrt{B^{2} - {4A\; C}}}{2A}\mspace{11mu} {if}\mspace{14mu} A} \neq 0}};\mspace{20mu} {p_{1}^{o_{3}} = {{\frac{- C}{B}\mspace{14mu} {if}\mspace{14mu} A} = 0}};{A = {\frac{g_{2B}}{\ln \; 2}\left\lbrack {{g_{11}g_{12}{g_{2B}\left( {{n_{1}g_{2B}} + {g_{21}\Gamma}} \right)}\left( {{g_{12}g_{2B}} - {g_{22}g_{1B}}} \right)} - {g_{22}g_{21}{g_{1B}\left( {{g_{1B}n_{2}} + {g_{12}\Gamma}} \right)}\left( {{g_{21}g_{1B}} - {g_{11}g_{2B}}} \right)}} \right\rbrack}};{B = {\frac{g_{2B}\left( {{n_{1}g_{2B}} + {g_{21}\Gamma}} \right)}{\ln \; 2} \times \left\lbrack {{g_{11}{g_{2B}\left( {{2g_{12}g_{2B}n_{2}} + {g_{12}g_{22}\Gamma} - {g_{22}g_{1B}n_{2}}} \right)}} - {{g_{22}\left( {{g_{1B}n_{2}} + {g_{12}\Gamma}} \right)}\left( {{g_{11}g_{2B}} - {2g_{21}g_{1B}}} \right)}} \right\rbrack}};{C = {\frac{g_{2B}\left( {{n_{1}g_{2B}} + {g_{21}\Gamma}} \right)}{\ln \; 2} \times {\left\lbrack {{g_{11}g_{2B}{n_{2}\left( {{n_{2}g_{2B}} + {g_{22}\Gamma}} \right)}} - {{g_{22}\left( {{g_{1B}n_{2}} + {g_{12}\Gamma}} \right)}\left( {{n_{1}g_{2B}} + {g_{21}\Gamma}} \right)}} \right\rbrack.}}}} \right.}} \right.}} \right.}} \right.}$
 9. The method according to claim 4, wherein the maximizing of the two-user throughput further comprises: when f_(l) _(o) ((Y₁)_(h))≧(Y₁)_(v) and f_(l) _(o) ((Y₂)_(h))≦(Y₂)_(v), computing the optimal power levels of SU 1 and SU 2, respectively denoted as p*₁ and p*₂, for maximizing the two-user throughput by $\mspace{20mu} {\left( {p_{1}^{*},p_{2}^{*}} \right) = \left\{ {\begin{matrix} {\arg \; {\max\limits_{{({p_{1},p_{2}})} \in {\{{Y_{0},Y_{2},Z_{1},O_{1}^{I,4},O_{2}^{I,4}}\}}}{F\left( {p_{1},p_{2}} \right)}}} & {{{{if}\mspace{14mu} A} \neq 0},} \\ {\arg \; {\max\limits_{{({p_{1},p_{2}})} \in {\{{Y_{0},Y_{2},Z_{1},O_{3}^{I,4}}\}}}{F\left( {p_{1},p_{2}} \right)}}} & {{{if}\mspace{14mu} A} = 0} \end{matrix};\mspace{20mu} {{{where}\text{:}\mspace{20mu} Y_{0}} = {{\left( {p_{1}^{m\; {ax}},\frac{\Gamma - {g_{1B}p_{1}^{{ma}\; x}}}{g_{2B}\;}} \right)\mspace{14mu} {and}\mspace{14mu} Y_{0}} = \left( {\left( Y_{0} \right)_{h},\left( Y_{0} \right)_{v}} \right)}};\mspace{20mu} {Y_{1} = {{\left( {p_{1}^{m\; {ax}},\frac{{g_{11}p_{1}^{{ma}\; x}} - {\gamma_{1}n_{1}}}{\gamma_{1}g_{21\;}}} \right)\mspace{14mu} {and}\mspace{14mu} Y_{1}} = \left( {\left( Y_{1} \right)_{h},\left( Y_{1} \right)_{v}} \right)}};\mspace{20mu} {Y_{2} = {{\left( {p_{1}^{{ma}\; x},\frac{{n_{2}\gamma_{2}} + {g_{12}\gamma_{2}p_{1}^{{ma}\; x}}}{g_{22}}} \right)\mspace{14mu} {and}\mspace{14mu} Y_{2}} = \left( {\left( Y_{2} \right)_{h},\left( Y_{2} \right)_{v}} \right)}};\mspace{20mu} {Z_{1} = {{\left( {\frac{{g_{21}\gamma_{1}\Gamma} + {g_{2B}n_{1}\gamma_{1}}}{{g_{11}g_{2B}} + {g_{1B}g_{21}\gamma_{1}}},\frac{{g_{11}\Gamma} - {g_{1B}n_{1}\gamma_{1}}}{{g_{11}g_{2B}} + {g_{1B}g_{21}\gamma_{1}}}} \right)\mspace{20mu} {and}\mspace{14mu} Z_{1}} = \left( {\left( Z_{1} \right)_{h},\left( Z_{1} \right)_{v}} \right)}};\mspace{20mu} {O_{1}^{I,4} = \left\{ {\begin{matrix} \left( {p_{1}^{o_{1}},{f_{l_{0}}\left( p_{1}^{o_{1}} \right)}} \right) & {{{if}\mspace{14mu} \left( Z_{1} \right)_{h}} < p_{1}^{o_{1}} < \left( Y_{0} \right)_{h}} \\ \varphi & {otherwise} \end{matrix};\mspace{20mu} {O_{2}^{I,4} = \left\{ {\begin{matrix} \left( {p_{1}^{o_{2}},{f_{l_{0}}\left( p_{1}^{o_{2}} \right)}} \right) & {{{if}\mspace{14mu} \left( Z_{1} \right)_{h}} < p_{1}^{o_{2}} < \left( Y_{0} \right)_{h}} \\ \varphi & {otherwise} \end{matrix};\mspace{20mu} {O_{3}^{I,4} = \left\{ {\begin{matrix} \left( {p_{1}^{o_{3}},{f_{l_{0}}\left( p_{1}^{o_{3}} \right)}} \right) & {{{if}\mspace{14mu} \left( Z_{1} \right)_{h}} < p_{1}^{o_{3}} < \left( Y_{0} \right)_{h}} \\ \varphi & {otherwise} \end{matrix};\mspace{20mu} {p_{1}^{o_{1}} = {{\frac{{- B} + \sqrt{B^{2} - {4A\; C}}}{2A}\mspace{14mu} {if}\mspace{14mu} A} \neq 0}};\mspace{20mu} {p_{1}^{o_{2}} = {{\frac{{- B} - \sqrt{B^{2} - {4A\; C}}}{2A}\mspace{11mu} {if}\mspace{14mu} A} \neq 0}};\mspace{20mu} {p_{1}^{o_{3}} = {{\frac{- C}{B}\mspace{14mu} {if}\mspace{14mu} A} = 0}};{A = {\frac{g_{2B}}{\ln \; 2}\left\lbrack {{g_{11}g_{12}{g_{2B}\left( {{n_{1}g_{2B}} + {g_{21}\Gamma}} \right)}\left( {{g_{12}g_{2B}} - {g_{22}g_{1B}}} \right)} - {g_{22}g_{21}{g_{1B}\left( {{g_{1B}n_{2}} + {g_{12}\Gamma}} \right)}\left( {{g_{21}g_{1B}} - {g_{11}g_{2B}}} \right)}} \right\rbrack}};{B = {\frac{g_{2B}\left( {{n_{1}g_{2B}} + {g_{21}\Gamma}} \right)}{\ln \; 2} \times \left\lbrack {{g_{11}{g_{2B}\left( {{2g_{12}g_{2B}n_{2}} + {g_{12}g_{22}\Gamma} - {g_{22}g_{1B}n_{2}}} \right)}} - {{g_{22}\left( {{g_{1B}n_{2}} + {g_{12}\Gamma}} \right)}\left( {{g_{11}g_{2B}} - {2g_{21}g_{1B}}} \right)}} \right\rbrack}};{C = {\frac{g_{2B}\left( {{n_{1}g_{2B}} + {g_{21}\Gamma}} \right)}{\ln \; 2} \times {\left\lbrack {{g_{11}g_{2B}{n_{2}\left( {{n_{2}g_{2B}} + {g_{22}\Gamma}} \right)}} - {{g_{22}\left( {{g_{1B}n_{2}} + {g_{12}\Gamma}} \right)}\left( {{n_{1}g_{2B}} + {g_{21}\Gamma}} \right)}} \right\rbrack.}}}} \right.}} \right.}} \right.}} \right.}$
 10. The method according to claim 4, wherein the maximizing of the two-user throughput further comprises: when f_(l) _(o) ((Y₁)_(h))≧(Y₁)_(v) and f_(l) _(o) ((Y₂)_(h))≦(Y₂)_(v), computing the optimal power levels of SU 1 and SU 2, respectively denoted as p*₁ and p*₂, for maximizing the two-user throughput by $\mspace{20mu} {\left( {p_{1}^{*},p_{2}^{*}} \right) = \left\{ {\begin{matrix} {\arg \; {\max\limits_{{({p_{1},p_{2}})} \in {\{{Z_{1},Z_{2},O_{1}^{I,2},O_{2}^{I,2}}\}}}{F\left( {p_{1},p_{2}} \right)}}} & {{{{if}\mspace{14mu} A} \neq 0},} \\ {\arg \; {\max\limits_{{({p_{1},p_{2}})} \in {\{{Z_{1},Z_{2},O_{3}^{I,2}}\}}}{F\left( {p_{1},p_{2}} \right)}}} & {{{if}\mspace{14mu} A} = 0} \end{matrix};\mspace{20mu} {{{where}\text{:}\mspace{20mu} Y_{1}} = {{\left( {p_{1}^{m\; {ax}},\frac{{g_{11}p_{1}^{{ma}\; x}} - {\gamma_{1}n_{1}}}{\gamma_{1}g_{21}}} \right)\mspace{14mu} {and}\mspace{14mu} Y_{1}} = \left( {\left( Y_{1} \right)_{h},\left( Y_{1} \right)_{v}} \right)}};\mspace{20mu} {Y_{2} = {{\left( {p_{1}^{{ma}\; x},\frac{{n_{2}\gamma_{2}} + {g_{12}\gamma_{2}p_{1}^{{ma}\; x}}}{g_{22}}} \right)\mspace{14mu} {and}\mspace{14mu} Y_{2}} = \left( {\left( Y_{2} \right)_{h},\left( Y_{2} \right)_{v}} \right)}};\mspace{20mu} {Z_{1} = {{\left( {\frac{{g_{21}\gamma_{1}\Gamma} + {g_{2B}n_{1}\gamma_{1}}}{{g_{11}g_{2B}} + {g_{1B}g_{21}\gamma_{1}}},\frac{{g_{11}\Gamma} - {g_{1B}n_{1}\gamma_{1}}}{{g_{11}g_{2B}} + {g_{1B}g_{21}\gamma_{1}}}} \right)\mspace{20mu} {and}\mspace{14mu} Z_{1}} = \left( {\left( Z_{1} \right)_{h},\left( Z_{1} \right)_{v}} \right)}};\mspace{20mu} {Z_{2} = {{\left( {\frac{{g_{22}\Gamma} - {g_{2B}n_{2}\gamma_{2}}}{{g_{22}g_{1B}} + {g_{12}g_{12}\gamma_{2}}},\frac{{g_{12}\gamma_{2}\Gamma} + {g_{1B}n_{2}\gamma_{2}}}{{g_{22}g_{1B}} + {g_{2B}g_{12}\gamma_{2}}}} \right)\mspace{20mu} {and}\mspace{14mu} Z_{2}} = \left( {\left( Z_{2} \right)_{h},\left( Z_{2} \right)_{v}} \right)}};\mspace{20mu} {O_{1}^{{I,2}\;} = \left\{ {\begin{matrix} \left( {p_{1}^{o_{1}},{f_{l_{0}}\left( p_{1}^{o_{1}} \right)}} \right) & {{{if}\mspace{14mu} \left( Z_{1} \right)_{h}} < p_{1}^{o_{1}} < \left( Z_{2} \right)_{h}} \\ \varphi & {otherwise} \end{matrix};\mspace{20mu} {O_{2}^{I,2} = \left\{ {\begin{matrix} \left( {p_{1}^{o_{2}},{f_{l_{0}}\left( p_{1}^{o_{2}} \right)}} \right) & {{{if}\mspace{14mu} \left( Z_{1} \right)_{h}} < p_{1}^{o_{2}} < \left( Z_{2} \right)_{h}} \\ \varphi & {otherwise} \end{matrix};\mspace{20mu} {O_{3}^{I,2} = \left\{ {\begin{matrix} \left( {p_{1}^{o_{3}},{f_{l_{0}}\left( p_{1}^{o_{3}} \right)}} \right) & {{{if}\mspace{14mu} \left( Z_{1} \right)_{h}} < p_{1}^{o_{3}} < \left( Z_{2} \right)_{h}} \\ \varphi & {otherwise} \end{matrix};\mspace{20mu} {p_{1}^{o_{1}} = {{\frac{{- B} + \sqrt{B^{2} - {4A\; C}}}{2A}\mspace{14mu} {if}\mspace{14mu} A} \neq 0}};\mspace{20mu} {p_{1}^{o_{2}} = {{\frac{{- B} - \sqrt{B^{2} - {4A\; C}}}{2A}\mspace{11mu} {if}\mspace{14mu} A} \neq 0}};\mspace{20mu} {p_{1}^{o_{3}} = {{\frac{- C}{B}\mspace{14mu} {if}\mspace{14mu} A} = 0}};{A = {\frac{g_{2B}}{\ln \; 2}\left\lbrack {{g_{11}g_{12}{g_{2B}\left( {{n_{1}g_{2B}} + {g_{21}\Gamma}} \right)}\left( {{g_{12}g_{2B}} - {g_{22}g_{1B}}} \right)} - {g_{22}g_{21}{g_{1B}\left( {{g_{1B}n_{2}} + {g_{12}\Gamma}} \right)}\left( {{g_{21}g_{1B}} - {g_{11}g_{2B}}} \right)}} \right\rbrack}};{B = {\frac{g_{2B}\left( {{n_{1}g_{2B}} + {g_{21}\Gamma}} \right)}{\ln \; 2} \times \left\lbrack {{g_{11}{g_{2B}\left( {{2g_{12}g_{2B}n_{2}} + {g_{12}g_{22}\Gamma} - {g_{22}g_{1B}n_{2}}} \right)}} - {{g_{22}\left( {{g_{1B}n_{2}} + {g_{12}\Gamma}} \right)}\left( {{g_{11}g_{2B}} - {2g_{21}g_{1B}}} \right)}} \right\rbrack}};{C = {\frac{g_{2B}\left( {{n_{1}g_{2B}} + {g_{21}\Gamma}} \right)}{\ln \; 2} \times {\left\lbrack {{g_{11}g_{2B}{n_{2}\left( {{n_{2}g_{2B}} + {g_{22}\Gamma}} \right)}} - {{g_{22}\left( {{g_{1B}n_{2}} + {g_{12}\Gamma}} \right)}\left( {{n_{1}g_{2B}} + {g_{21}\Gamma}} \right)}} \right\rbrack.}}}} \right.}} \right.}} \right.}} \right.}$
 11. The method according to claim 4, wherein the maximizing of the two-user throughput further comprises: when f_(l) _(o) ((Y₁)_(h))≧(Y₁)_(v) and f_(l) _(o) ((Y₂)_(h))≧(Y₂)_(v), computing the optimal power levels of SU 1 and SU 2, respectively denoted as p*₁ and p*₂, for maximizing the two-user throughput by ${\left( {p_{1}^{*},p_{2}^{*}} \right) = {\arg \; {\max\limits_{{({p_{1},p_{2}})} \in {\lbrack{Y_{1},Y_{2}}\rbrack}}{F\left( {p_{1},p_{2}} \right)}}}};$ where: ${Y_{1} = {{\left( {p_{1}^{m\; {ax}},\frac{{g_{11}p_{1}^{{ma}\; x}} - {\gamma_{1}n_{1}}}{\gamma_{1}g_{21}}} \right)\mspace{14mu} {and}\mspace{14mu} Y_{1}} = \left( {\left( Y_{1} \right)_{h},\left( Y_{1} \right)_{v}} \right)}};$ $Y_{2} = {{\left( {p_{1}^{{ma}\; x},\frac{{n_{2}\gamma_{2}} + {g_{12}\gamma_{2}p_{1}^{{ma}\; x}}}{g_{22}}} \right)\mspace{14mu} {and}\mspace{14mu} Y_{2}} = {\left( {\left( Y_{2} \right)_{h},\left( Y_{2} \right)_{v}} \right).}}$
 12. The method according to claim 4, wherein the maximizing of the two-user throughput further comprises: when f_(l) _(o) ((X₁)_(h))≧(X₁)_(v) and f_(l) _(o) ((X₂)_(h))≦(X₂)_(v), computing the optimal power levels of SU 1 and SU 2, respectively denoted as p*₁ and p*₂, for maximizing the two-user throughput by $\mspace{20mu} {\left( {p_{1}^{*},p_{2}^{*}} \right) = \left\{ {\begin{matrix} {\arg \; {\max\limits_{{({p_{1},p_{2}})} \in {\{{X_{0},X_{1},Z_{2},O_{1}^{I,3},O_{2}^{I,3}}\}}}{F\left( {p_{1},p_{2}} \right)}}} & {{{{if}\mspace{14mu} A} \neq 0},} \\ {\arg \; {\max\limits_{{({p_{1},p_{2}})} \in {\{{X_{0},X_{1},Z_{2},O_{3}^{I,3}}\}}}{F\left( {p_{1},p_{2}} \right)}}} & {{{if}\mspace{14mu} A} = 0} \end{matrix};\mspace{20mu} {{{where}\text{:}\mspace{20mu} X_{0}} = {{\left( {\frac{\Gamma - {g_{2B}p_{2}^{{ma}\; x}}}{g_{1B}},p_{2}^{{ma}\; x}} \right)\mspace{14mu} {and}\mspace{14mu} X_{0}} = \left( {\left( X_{0} \right)_{h},\left( X_{0} \right)_{v}} \right)}};\mspace{20mu} {X_{1} = {{\left( {\frac{{\gamma_{1}n_{1}} + {g_{21}\gamma_{1}p_{2}^{{ma}\; x}}}{g_{11}},p_{2}^{{ma}\; x}} \right)\mspace{14mu} {and}\mspace{14mu} X_{1}} = \left( {\left( X_{1} \right)_{h},\left( X_{1} \right)_{v}} \right)}};\mspace{20mu} {X_{2} = {{\left( {\frac{{g_{22}p_{2}^{{ma}\; x}} - {\gamma_{2}n_{2}}}{\gamma_{2}g_{12}},p_{2}^{{ma}\; x}} \right)\mspace{14mu} {and}\mspace{14mu} X_{2}} = \left( {\left( X_{2} \right)_{h},\left( X_{2} \right)_{v}} \right)}};\mspace{20mu} {Z_{2} = {{\left( {\frac{{g_{22}\Gamma} - {g_{2B}n_{2}\gamma_{2}}}{{g_{22}g_{1B}} + {g_{12}g_{12}\gamma_{2}}},\frac{{g_{12}\gamma_{2}\Gamma} + {g_{1B}n_{2}\gamma_{2}}}{{g_{22}g_{1B}} + {g_{2B}g_{12}\gamma_{2}}}} \right)\mspace{20mu} {and}\mspace{14mu} Z_{2}} = \left( {\left( Z_{2} \right)_{h},\left( Z_{2} \right)_{v}} \right)}};\mspace{20mu} {O_{1}^{{I,3}\;} = \left\{ {\begin{matrix} \left( {p_{1}^{o_{1}},{f_{l_{0}}\left( p_{1}^{o_{1}} \right)}} \right) & {{{if}\mspace{14mu} \left( X_{0} \right)_{h}} < p_{1}^{o_{1}} < \left( Z_{2} \right)_{h}} \\ \varphi & {otherwise} \end{matrix};\mspace{20mu} {O_{2}^{I,3} = \left\{ {\begin{matrix} \left( {p_{1}^{o_{2}},{f_{l_{0}}\left( p_{1}^{o_{2}} \right)}} \right) & {{{if}\mspace{14mu} \left( X_{0} \right)_{h}} < p_{1}^{o_{2}} < \left( Z_{2} \right)_{h}} \\ \varphi & {otherwise} \end{matrix};\mspace{20mu} {O_{3}^{I,3} = \left\{ {\begin{matrix} \left( {p_{1}^{o_{3}},{f_{l_{0}}\left( p_{1}^{o_{3}} \right)}} \right) & {{{if}\mspace{14mu} \left( X_{0} \right)_{h}} < p_{1}^{o_{3}} < \left( Z_{2} \right)_{h}} \\ \varphi & {otherwise} \end{matrix};\mspace{20mu} {p_{1}^{o_{1}} = {{\frac{{- B} + \sqrt{B^{2} - {4A\; C}}}{2A}\mspace{14mu} {if}\mspace{14mu} A} \neq 0}};\mspace{20mu} {p_{1}^{o_{2}} = {{\frac{{- B} - \sqrt{B^{2} - {4A\; C}}}{2A}\mspace{11mu} {if}\mspace{14mu} A} \neq 0}};\mspace{20mu} {p_{1}^{o_{3}} = {{\frac{- C}{B}\mspace{14mu} {if}\mspace{14mu} A} = 0}};{A = {\frac{g_{2B}}{\ln \; 2}\left\lbrack {{g_{11}g_{12}{g_{2B}\left( {{n_{1}g_{2B}} + {g_{21}\Gamma}} \right)}\left( {{g_{12}g_{2B}} - {g_{22}g_{1B}}} \right)} - {g_{22}g_{21}{g_{1B}\left( {{g_{1B}n_{2}} + {g_{12}\Gamma}} \right)}\left( {{g_{21}g_{1B}} - {g_{11}g_{2B}}} \right)}} \right\rbrack}};{B = {\frac{g_{2B}\left( {{n_{1}g_{2B}} + {g_{21}\Gamma}} \right)}{\ln \; 2} \times \left\lbrack {{g_{11}{g_{2B}\left( {{2g_{12}g_{2B}n_{2}} + {g_{12}g_{22}\Gamma} - {g_{22}g_{1B}n_{2}}} \right)}} - {{g_{22}\left( {{g_{1B}n_{2}} + {g_{12}\Gamma}} \right)}\left( {{g_{11}g_{2B}} - {2g_{21}g_{1B}}} \right)}} \right\rbrack}};{C = {\frac{g_{2B}\left( {{n_{1}g_{2B}} + {g_{21}\Gamma}} \right)}{\ln \; 2} \times {\left\lbrack {{g_{11}g_{2B}{n_{2}\left( {{n_{2}g_{2B}} + {g_{22}\Gamma}} \right)}} - {{g_{22}\left( {{g_{1B}n_{2}} + {g_{12}\Gamma}} \right)}\left( {{n_{1}g_{2B}} + {g_{21}\Gamma}} \right)}} \right\rbrack.}}}} \right.}} \right.}} \right.}} \right.}$
 13. The method according to claim 4, wherein the maximizing of the two-user throughput further comprises: when f_(l) _(o) ((X₁)_(h))≦(X₁)_(v) and f_(l) _(o) ((X₂)_(h))≦(X₂)_(v), computing the optimal power levels of SU 1 and SU 2, respectively denoted as p*₁ and p*₂, for maximizing the two-user throughput by $\mspace{20mu} {\left( {p_{1}^{*},p_{2}^{*}} \right) = \left\{ {\begin{matrix} {\arg \; {\max\limits_{{({p_{1},p_{2}})} \in {\{{Z_{1},Z_{2},O_{1}^{I,2},O_{2}^{I,2}}\}}}{F\left( {p_{1},p_{2}} \right)}}} & {{{{if}\mspace{14mu} A} \neq 0},} \\ {\arg \; {\max\limits_{{({p_{1},p_{2}})} \in {\{{Z_{1},Z_{2},O_{3}^{I,2}}\}}}{F\left( {p_{1},p_{2}} \right)}}} & {{{if}\mspace{14mu} A} = 0} \end{matrix};\mspace{20mu} {{{where}\text{:}\mspace{20mu} X_{1}} = {{\left( {\frac{{\gamma_{1}n_{1}} + {g_{21}\gamma_{1}p_{2}^{{ma}\; x}}}{g_{11}},p_{2}^{{ma}\; x}} \right)\mspace{14mu} {and}\mspace{14mu} X_{1}} = \left( {\left( X_{1} \right)_{h},\left( X_{1} \right)_{v}} \right)}};\mspace{20mu} {X_{2} = {{\left( {\frac{{g_{22}p_{2}^{{ma}\; x}} - {\gamma_{2}n_{2}}}{\gamma_{2}g_{12}},p_{2}^{{ma}\; x}} \right)\mspace{14mu} {and}\mspace{14mu} X_{2}} = \left( {\left( X_{2} \right)_{h},\left( X_{2} \right)_{v}} \right)}};\mspace{20mu} {Z_{1} = {{\left( {\frac{{g_{21}\gamma_{1}\Gamma} + {g_{2B}n_{1}\gamma_{1}}}{{g_{11}g_{2B}} + {g_{1B}g_{21}\gamma_{1}}},\frac{{g_{11}\Gamma} - {g_{1B}n_{1}\gamma_{1}}}{{g_{11}g_{2B}} + {g_{1B}g_{21}\gamma_{1}}}} \right)\mspace{20mu} {and}\mspace{14mu} Z_{1}} = \left( {\left( Z_{1} \right)_{h},\left( Z_{1} \right)_{v}} \right)}};\mspace{20mu} {Z_{2} = {{\left( {\frac{{g_{22}\Gamma} - {g_{2B}n_{2}\gamma_{2}}}{{g_{22}g_{1B}} + {g_{12}g_{12}\gamma_{2}}},\frac{{g_{12}\gamma_{2}\Gamma} + {g_{1B}n_{2}\gamma_{2}}}{{g_{22}g_{1B}} + {g_{2B}g_{12}\gamma_{2}}}} \right)\mspace{20mu} {and}\mspace{14mu} Z_{2}} = \left( {\left( Z_{2} \right)_{h},\left( Z_{2} \right)_{v}} \right)}};\mspace{20mu} {O_{1}^{{I,1}\;} = \left\{ {\begin{matrix} \left( {p_{1}^{o_{1}},{f_{l_{0}}\left( p_{1}^{o_{1}} \right)}} \right) & {{{if}\mspace{14mu} \left( Z_{1} \right)_{h}} < p_{1}^{o_{1}} < \left( Z_{2} \right)_{h}} \\ \varphi & {otherwise} \end{matrix};\mspace{20mu} {O_{2}^{I,1} = \left\{ {\begin{matrix} \left( {p_{1}^{o_{2}},{f_{l_{0}}\left( p_{1}^{o_{2}} \right)}} \right) & {{{if}\mspace{14mu} \left( Z_{1} \right)_{h}} < p_{1}^{o_{2}} < \left( Z_{2} \right)_{h}} \\ \varphi & {otherwise} \end{matrix};\mspace{20mu} {O_{3}^{I,1} = \left\{ {\begin{matrix} \left( {p_{1}^{o_{3}},{f_{l_{0}}\left( p_{1}^{o_{3}} \right)}} \right) & {{{if}\mspace{14mu} \left( Z_{1} \right)_{h}} < p_{1}^{o_{3}} < \left( Z_{2} \right)_{h}} \\ \varphi & {otherwise} \end{matrix};\mspace{20mu} {p_{1}^{o_{1}} = {{\frac{{- B} + \sqrt{B^{2} - {4A\; C}}}{2A}\mspace{14mu} {if}\mspace{14mu} A} \neq 0}};\mspace{20mu} {p_{1}^{o_{2}} = {{\frac{{- B} - \sqrt{B^{2} - {4A\; C}}}{2A}\mspace{11mu} {if}\mspace{14mu} A} \neq 0}};\mspace{20mu} {p_{1}^{o_{3}} = {{\frac{- C}{B}\mspace{14mu} {if}\mspace{14mu} A} = 0}};{A = {\frac{g_{2B}}{\ln \; 2}\left\lbrack {{g_{11}g_{12}{g_{2B}\left( {{n_{1}g_{2B}} + {g_{21}\Gamma}} \right)}\left( {{g_{12}g_{2B}} - {g_{22}g_{1B}}} \right)} - {g_{22}g_{21}{g_{1B}\left( {{g_{1B}n_{2}} + {g_{12}\Gamma}} \right)}\left( {{g_{21}g_{1B}} - {g_{11}g_{2B}}} \right)}} \right\rbrack}};{B = {\frac{g_{2B}\left( {{n_{1}g_{2B}} + {g_{21}\Gamma}} \right)}{\ln \; 2} \times \left\lbrack {{g_{11}{g_{2B}\left( {{2g_{12}g_{2B}n_{2}} + {g_{12}g_{22}\Gamma} - {g_{22}g_{1B}n_{2}}} \right)}} - {{g_{22}\left( {{g_{1B}n_{2}} + {g_{12}\Gamma}} \right)}\left( {{g_{11}g_{2B}} - {2g_{21}g_{1B}}} \right)}} \right\rbrack}};{C = {\frac{g_{2B}\left( {{n_{1}g_{2B}} + {g_{21}\Gamma}} \right)}{\ln \; 2} \times {\left\lbrack {{g_{11}g_{2B}{n_{2}\left( {{n_{2}g_{2B}} + {g_{22}\Gamma}} \right)}} - {{g_{22}\left( {{g_{1B}n_{2}} + {g_{12}\Gamma}} \right)}\left( {{n_{1}g_{2B}} + {g_{21}\Gamma}} \right)}} \right\rbrack.}}}} \right.}} \right.}} \right.}} \right.}$
 14. The method according to claim 4, wherein the maximizing of the two-user throughput further comprises: when f_(l) _(o) ((X₁)_(h))≧(X₁)_(v) and f_(l) _(o) ((X₂)_(h))≧(X₂)_(v), computing the optimal power levels of SU 1 and SU 2, respectively denoted as p*₁ and p*₂, for maximizing the two-user throughput by ${\left( {p_{1}^{*},p_{2}^{*}} \right) = {\arg \; {\max\limits_{{({p_{1},p_{2}})} \in {\lbrack{X_{1},X_{2}}\rbrack}}{F\left( {p_{1},p_{2}} \right)}}}};$ where: ${X_{1} = {{\left( {\frac{{\gamma_{1}n_{1}} + {g_{21}\gamma_{1}p_{2}^{{ma}\; x}}}{g_{11}},p_{2}^{{ma}\; x}} \right)\mspace{14mu} {and}\mspace{14mu} X_{1}} = \left( {\left( X_{1} \right)_{h},\left( X_{1} \right)_{v}} \right)}};$ $X_{2} = {{\left( {\frac{{g_{22}p_{2}^{{ma}\; x}} - {\gamma_{2}n_{2}}}{\gamma_{2}g_{12}},p_{2}^{{ma}\; x}} \right)\mspace{14mu} {and}\mspace{14mu} X_{2}} = {\left( {\left( X_{2} \right)_{h},\left( X_{2} \right)_{v}} \right).}}$ 